Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)

ABSTRACT

The present invention provides a technique for continuous measurement of blood pressure based on pulse transit time and which does not require any external calibration. This technique, referred to herein as the ‘Composite Method’, is carried out with a body-worn monitor that measures blood pressure and other vital signs, and wirelessly transmits them to a remote monitor. A network of body-worn sensors, typically placed on the patient&#39;s right arm and chest, connect to the body-worn monitor and measure time-dependent ECG, PPG, accelerometer, and pressure waveforms. The disposable sensors can include a cuff that features an inflatable bladder coupled to a pressure sensor, three or more electrical sensors (e.g. electrodes), three or more accelerometers, a temperature sensor, and an optical sensor (e.g., a light source and photodiode) attached to the patient&#39;s thumb.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a Continuation-in-Part of co-pending U.S. patentapplication Ser. No. 12/138,194, filed Jun. 12, 2008, entitled VITALSIGN MONITOR FOR MEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, ANDPRESSURE WAVEFORMS, which claims the benefit of U.S. ProvisionalApplication No. 60/943,464, filed Jun. 12, 2007, and of U.S. ProvisionalApplication No. 60/983,198, filed Oct. 28, 2007; all of which areincorporated herein by reference.

BACKGROUND OF THE INVENTION

Pulse transit time (PTT), defined as the transit time for a pressurepulse launched by a heartbeat in a patient's arterial system, has beenshown in a number of studies to correlate to both systolic and diastolicblood pressure. In these studies, PTT is typically measured with aconventional vital signs monitor that includes separate modules todetermine both an electrocardiogram (ECG waveform) and pulse oximetry(SpO2). During a PTT measurement, multiple electrodes typically attachto a patient's chest to determine a time-dependent component of the ECGwaveform characterized by a sharp spike called the ‘QRS complex’. TheQRS complex indicates an initial depolarization of ventricles within theheart and, informally, marks the beginning of the heartbeat and apressure pulse that follows. SpO2 is typically measured with a bandageor clothespin-shaped sensor that attaches to a patient's finger, andincludes optical systems operating in both red and infrared spectralregions. A photodetector measures radiation emitted from the opticalsystems that transmits through the patient's finger. Other body sites,e.g., the ear, forehead, and nose, can also be used in place of thefinger. During a measurement, a microprocessor analyses both red andinfrared radiation measured by the photodetector to determinetime-dependent waveforms corresponding to the different wavelengthscalled photoplethysmographs (‘PPG waveforms’). From these a SpO2 valueis calculated. Time-dependent features of the PPG waveform indicate bothpulse rate and a volumetric absorbance change in an underlying artery(e.g., in the finger) caused by the propagating pressure pulse.

Typical PTT measurements determine the time separating a maximum pointon the QRS complex (indicating the peak of ventricular depolarization)and a portion of the PPG waveform (indicating the arrival of thepressure pulse). PTT depends primarily on arterial compliance, thepropagation distance of the pressure pulse (which is closelyapproximated by the patient's arm length), and blood pressure. Toaccount for patient-specific properties, such as arterial compliance,PTT-based measurements of blood pressure are typically ‘calibrated’using a conventional blood pressure cuff. Typically during thecalibration process the blood pressure cuff is applied to the patient,used to make one or more blood pressure measurements, and then removed.Going forward, the calibration measurements are used, along with achange in PTT, to determine the patient's blood pressure and bloodpressure variability. PTT typically relates inversely to blood pressure,i.e., a decrease in PTT indicates an increase in blood pressure.

A number of issued U.S. patents describe the relationship between PTTand blood pressure. For example, U.S. Pat. Nos. 5,316,008; 5,857,975;5,865,755; and 5,649,543 each describe an apparatus that includesconventional sensors that measure ECG and PPG waveforms, which are thenprocessed to determine PTT.

SUMMARY OF THE INVENTION

This invention provides a technique for continuous measurement of bloodpressure (cNIBP), based on PTT, which features a number of improvementsover conventional PTT measurements. Referred to herein as the ‘CompositeMethod’, the invention uses a body-worn monitor that measures cNIBP andother vital signs, and wirelessly transmits them to a remote monitor,such as a tablet PC, workstation at a nursing station, personal digitalassistant (PDA), or cellular telephone. The body-worn monitor features awrist-worn transceiver that receives and processes signals generated bya network of body-worn sensors. During a measurement these sensors aretypically placed on the patient's arm and chest and measuretime-dependent ECG, PPG, pressure, and accelerometer waveforms. Sensorswithin the network typically include a cuff with an inflatable airbladder, at least three electrical sensors (e.g. ECG electrodes), threeaccelerometers, and an optical sensor (e.g., a light source andphotodiode) typically worn around the patient's thumb. They measuresignals that are processed according to the Composite Method todetermine blood pressure, and with other algorithms to determine vitalsigns such as SpO2, respiration rate, heart rate, temperature, andmotion-related properties such as motion, activity level, and posture.The body-worn monitor then wirelessly transmits this information(typically using a two-way wireless protocol, e.g. 802.15.4 or 802.11)to the remote monitor. The monitor displays both vital signs and thetime-dependent waveforms. Both the monitor and the wrist-worntransceiver can additionally include a barcode scanner, touch screendisplay, camera, voice and speaker system, and wireless systems thatoperate with both local-area networks (e.g. 802.11 or ‘WiFi’ networks)and wide-area networks (e.g. the Sprint network) to transmit and displayinformation.

The Composite Method includes both pressure-dependent and pressure-freemeasurements. It is based on the discovery that PTT and the PPG waveformused to determine it are strongly modulated by an applied pressure.During a pressure-dependent measurement, also referred to herein as an‘indexing measurement’, two events occur as the pressure graduallyincreases to the patient's systolic pressure: 1) PTT increases,typically in a non-linear manner, once the applied pressure exceedsdiastolic pressure; and 2) the magnitude of the PPG's amplitudesystematically decreases, typically in a linear manner, as the appliedpressure approaches systolic pressure. The applied pressure graduallydecreases blood flow and consequent blood pressure in the patient's arm,and therefore induces the pressure-dependent increase in PTT. Each ofthe resulting pairs of PTT/blood pressure readings measured during theperiod of applied pressure can be used as a calibration point. Moreover,when the applied pressure equals systolic blood pressure, the amplitudeof the PPG waveform is completely eliminated, and PTT is no longermeasurable. Collectively analyzing both PTT and the PPG waveform'samplitude over a suitable range, along with the pressure waveform usingtechniques borrowed from conventional oscillometry, yields the patient'ssystolic (SYS), diastolic (DIA), and mean (MAP) arterial pressures,along with a patient-specific slope relating PTT and MAP. From theseparameters the patient's cNIBP can be determined without using aconventional cuff.

A combination of several algorithmic features improves the efficacy ofthe Composite Method over conventional PTT measurements of cNIBP. Forexample, sophisticated, real-time digital filtering removeshigh-frequency noise from the PPG waveform, allowing its onset point tobe accurately detected. When processed along with the ECG waveform, thisensures measurement of an accurate PTT and, ultimately, cNIBP value. Thepressure-dependent indexing method, which is made during inflation ofthe arm-worn cuff, yields multiple data points relating PTT and bloodpressure during a short (˜60 second) measurement. Processing of thesedata points yields an accurate patient-specific slope relating PTT tocNIBP. Inclusion of multiple accelerometers yields a variety of signalsthat can determine features like arm height, motion, activity level, andposture that can be further processed to improve accuracy of the cNIBPcalculation, and additionally allow it to be performed in the presenceof motion artifacts. And a model based on femoral blood pressure, whichis more representative of pressure in the patient's core, can reduceeffects such as ‘pulse pressure amplification’ that can elevate bloodpressure measured at a patient's extremities.

The Composite Method can also include an ‘intermediate’pressure-dependent measurement wherein the cuff is partially inflated.This partially decreases the amplitude of the PPG waveform in atime-dependent manner. The amplitude's pressure-dependent decrease canthen be ‘fit’ with a numerical function to estimate the pressure atwhich the amplitude completely disappears, indicating systolic pressure.

For the pressure-dependent measurement, a small pneumatic systemattached to the cuff inflates the bladder to apply pressure to anunderlying artery according to the pressure waveform. The cuff istypically located on the patient's upper arm, proximal to the brachialartery, and time-dependent pressure is measured by an internal pressuresensor, such as an in-line Wheatstone bridge or strain gauge, within thepneumatic system. The pressure waveform gradually ramps up in a mostlylinear manner during inflation, and then slowly rapidly deflates througha ‘bleeder valve’ during deflation. During inflation, mechanicalpulsations corresponding to the patient's heartbeats couple into thebladder as the applied pressure approaches DIA. The mechanicalpulsations modulate the pressure waveform so that it includes a seriesof time-dependent oscillations. The oscillations are similar to thosemeasured with an automated blood pressure cuff using oscillometry, onlythey are measured during inflation rather than deflation. They areprocessed as described below to determine a ‘processed pressurewaveform’, from which MAP is determined directly, and SYS and DIA aredetermined indirectly.

Pressure-dependent measurements performed on inflation have severaladvantages to similar measurements performed on deflation, which areconvention. For example, inflation-based measurements are relativelyfast and comfortable compared to those made on deflation. Mostconventional cuff-based systems using deflation-based oscillometry takeroughly 4 times longer than the Composite Method's pressure-dependentmeasurement. Inflation-based measurements are possible because of theComposite Method's relatively slow inflation speed (typically 5-10mmHg/second) and high sensitivity of the pressure sensor used within thebody-worn monitor. Moreover, measurements made during inflation can beimmediately terminated once systolic blood pressure is calculated. Incontrast, conventional cuff-based measurements made during deflationtypically apply a pressure that far exceeds the patient's systolic bloodpressure; pressure within the cuff then slowly bleeds down below DIA tocomplete the measurement.

Pressure-free measurements immediately follow the pressure-dependentmeasurements, and are typically made by determining PTT with the sameoptical and electrical sensors used in the pressure-dependentmeasurements. Specifically, the body-worn monitor processes PTT andother properties of the PPG waveform, along with the patient-specificslope and measurements of SYS, DIA, and MAP made during thepressure-dependent measurement, to determine cNIBP.

In addition to blood pressure, the body-worn monitor measures heart rate(HR), SpO2, and respiratory rate from components of the ECG, PPG, andaccelerometer waveforms. A body-worn thermocouple measures temperature.These measurements, along with those used to process accelerometerwaveforms to determine motion, posture, and activity level, are madeusing algorithms described below.

In one aspect, the invention provides a body-worn monitor, described indetail below, which measures cNIBP from an ambulatory patient accordingto the Composite Method. The body-worn monitor features: (1) apressure-delivery and sensor system that applies a variable pressure tothe patient's arm and, in response, measures a time-dependent pressurewaveform; (2) a first sensor (e.g. an optical sensor) that generates afirst time-dependent waveform representing a flow of blood within thepatient; and (3) a second sensor (e.g. an ECG circuit and electrodes)that generates a second time-dependent waveform representing contractileproperties of the patient's heart. A processing component receivesinformation from these sensors, and processes it to: (1) determine a PTTbetween features in the first and second waveforms; (2) determine amathematical relationship between PTT and blood pressure in thepatient's core region (e.g. femoral artery); and iii) analyze a PTT andthe mathematical relationship to generate a blood pressure indicative ofthe patient's core region. The processing component is typically locatedin the wrist-worn transceiver.

In embodiments, the ECG circuit within the body-worn monitor features asingle circuit (e.g. an ASIC) that collects electrical signals from aseries of body-worn electrodes and coverts these signals into a digitalECG waveform. Such a circuit is typically worn directly on the patient'schest, and connects to the wrist-worn transceiver through a digital,serial interface (e.g. an interface based on a ‘control area network’,or ‘CAN’, system). The optical sensor typically includes optics formeasuring signals relating to both cNIBP and SpO2, and typicallyfeatures a ring-like form factor that comfortably wraps around the baseof the patient's thumb. All of these systems are described in detailbelow.

In embodiments, both the first and second sensors feature transducersfor measuring optical, pressure, acoustic, and electrical impedancesignals, as well as electrical components for measuring ECG waveforms.In general, PTT can be determined from various combinations of thesesignals, e.g. between any two signals measured by a transducer, orbetween an ECG waveform and a second signal measured by a transducer. Inpreferred embodiments, the first sensor measures a PPG waveform, thesecond sensor measures an ECG waveform, and the processing componentdetermines PTT from a QRS complex in an ECG waveform and an onset pointof the PPG waveform. The processing component then analyzes PTT measuredas pressure is applied to determine its relationship to MAP in thepatient's femoral artery. In embodiments, this relationship ischaracterized by the following Equation, or a mathematical derivativethereof:MAP_(femoral)=(m _(femoral)×PTT)−(m _(femoral)×PTT_(INDEX))+MAP_(INDEX)wherein MAP_(femoral) represents blood pressure in the patient's femoralartery, PTT represents pulse transit time measured from the first andsecond waveforms, PTT_(INDEX) represents a pulse transit time determinedbefore PTT (and typically immediately before the pressure-dependentindexing measurement), m_(femoral) represents a mathematical sloperepresenting a relationship between MAP_(femoral) and PTT, andMAP_(INDEX) represents a mean arterial pressure determined from thetime-dependent pressure waveform. In the Equation above, m_(femoral) istypically determined by collectively processing the first, second, andpressure waveforms. For example, it can be determined by processing aset of PTT values measured while time-dependent pressure is applied tothe patient's arm, and then fitting the set with a linear equation toestimate a patient-specific relationship between PTT and MAP. Thisrelationship, which is determined during the pressure-dependent indexingmeasurement, forms part of a ‘calibration’ for cuffless, PTT-based cNIBPmeasurement made afterwards. Other calibration parameters determinedduring the indexing measurement are SYS, DIA, and relationships betweenthese parameters and MAP. These values are determined directly from apressure waveform, typically measured during inflation using techniquesderived from oscillometry. In embodiments, during an indexingmeasurement a digital filter, typically implemented with asoftware-based algorithm, processes the time-dependent pressure waveformto determine a ‘processed pressure waveform’. The digital filter, forexample, can be a 2-stage filter featuring a digital bandpass filter,followed by a digital low-pass filter. From the processed pressurewaveform SYS, DIA, and MAP can be determined.

In other embodiments, the relationship between SYS, DIA, and MAP dependson the patient's HR, which is typically determined from either the ECGor PPG waveform. In still other embodiments, the relationship betweenPTT and MAP is non-adjustable and determined beforehand, e.g. from agroup of patients in a clinical study. During an actual measurement,such a relationship is typically used as a default case when apatient-specific relationship cannot be accurately determined (because,e.g., of PPG or ECG waveforms corrupted by motion-related noise).Typically the relationship between PTT and MAP in the patient's femoralartery is between 0.5 mmHg/ms and 1.5 mmHg/ms.

In another aspect, the patient-specific indexing measurement involvesestimating an ‘effective MAP’ in the patient's arm that varies withpressure applied by the pressure-delivery system. The effective MAP isthe difference between MAP determined during the inflation in theindexing measurement and a pressure-induced blood pressure change,caused by an arm-worn cuff featuring an inflatable bladder. Inembodiments, the pressure-induced blood pressure change is defined bythe following equation or a mathematical derivative thereof:ΔMAP(P)=F×(P _(applied)−DIA_(INDEX))where ΔMAP(P) is the pressure-induced blood pressure change, P_(applied)is pressure applied by the pressure-delivery system during inflation,DIA_(INDEX) is the diastolic pressure determined from the processedpressure waveform during the indexing measurement, and F is amathematical constant.

In embodiments, the indexing measurement is performed once every 4 hoursor more, and a PTT-based cNIBP measurement is performed once every 1second or less. Typically, PTT values are averaged from a set of valuescollected over a time period between, typically ranging from 10 to 120seconds. The average is typically a ‘rolling average’ so that a newvalue, determined over the averaging period, can be displayed relativelyfrequently (e.g. every second).

In another aspect, the invention provides a method for monitoring ablood pressure value from a patient, which features determining a PTTvalue from a patient, as described above, from PPG and ECG waveforms.Additionally, HR is determined by analyzing QRS complexes in the ECGwaveform. During the measurement, the processing component determines amathematical relationship between HR (or a parameter calculatedtherefrom), and PTT (or a parameter calculated therefrom). At a laterpoint in time, the processing component uses the mathematicalrelationship and a current value of HR to estimate PTT and, ultimately,a based blood pressure value. This method would be deployed, forexample, when motion-related noise corrupts the PPG waveform (which isrelatively sensitive to motion), but not the ECG waveform (which isrelatively immune to motion).

In embodiments, the method measures a first set of HR values and asecond set of PTT values, and then processes the first and second setsto determine the mathematical relationship between them. The first andsecond sets are typically measured prior to measuring the HR used toestimate PTT, and are typically collected over a time period rangingbetween 5 and 60 seconds. Paired HR/PTT values collected during the timeperiod are then analyzed, typically by fitting them using a linearregression algorithm, to determine a mathematical relationship relatingHR to PTT. Alternatively a non-linear fitting algorithm, such as theLevenburg-Marquardt algorithm, can be used to determine a non-linearrelationship between HR and PTT. The non-linear relationship can becharacterized, e.g., by a second or third-order polynomial, or by anexponential function.

As described above, this algorithm is typically performed when apatient's motion makes it difficult or impossible to accuratelycalculate PTT from the PPG waveform. The algorithm can be initiated whenanalysis of a pulse in the PPG waveform indicates PTT cannot bemeasured. Alternatively, the algorithm is initiated when analysis of atleast one ‘motion waveform’ (e.g. an accelerometer waveform generatedfrom one or more signals from an accelerometer) indicates that the PPGwaveform is likely corrupted by motion. Analysis of the motion waveformcan involve comparing a portion of it to a predetermined threshold, oranalyzing it with a mathematical model, to determine if an accurate PTTcan be calculated.

In a related aspect, the invention provides another algorithm thatallows PTT-based cNIBP to be determined in the presence of motion. Inthis case, rather than estimating PTT from HR using a mathematicalmodel, the algorithm ‘reconstructs’ motion-corrupted pulses in the PPGwaveform through analysis of separate PPG waveforms measuredsimultaneously with two separate light sources. A pulse oximeter sensor,such as that included in the body-worn monitor described in detailbelow, includes a first light source operating in a red spectral region(between 590 and 700 nm, and preferably about 660 nm), and a secondlight source operating in the infrared spectral region (between 800 and1000 nm, and preferably around 905 nm), and can therefore be used forthis purpose.

The algorithm features: 1) collectively processing unique PPG waveformsto generate a processed signal; 2) processing the processed signal witha digital filter to generate a filtered signal; 3) analyzing thefiltered signal to determine a feature related to blood pressure; and 4)analyzing the feature related to blood pressure to determine the bloodpressure value. In embodiments, the processing component is programmedto collectively process the first and second signals by subtracting onesignal from the other, or dividing one signal into the other, togenerate the processed signal. This signal is then filtered with adigital bandpass filter, typically characterized by a passband between0.01→5.0 Hz, to generate the filtered signal. The filtered signal istypically relatively free of motion artifacts, and yields an onset pointwhich can be combined with an ECG QRS complex to determine PTT and thencNIBP. As described above, this algorithm can be initiated by processingan accelerometer waveform which indicates that a patient is moving, orby processing the PPG waveforms to determine that they are corrupted inany way. In other embodiments, steps in the algorithm are rearranged sothat the corrupted PPG waveforms are first filtered with a digitalbandpass filter, and then these filtered waveforms are subtracted fromeach other or divided into each other, and then processed to determinean onset point.

In another aspect, the body-worn monitor's optical sensor describedabove features a detector that includes at least two pixel elements,each configured to generate a unique signal. A processing componentwithin the monitor is configured to: (1) analyze a signal generated by afirst pixel element; (2) analyze a signal generated by a second pixelelement; (3) analyze a signal indicating motion, e.g. an accelerometerwaveform; (4) based on analysis of the motion signal, select a signalfrom at least one of the pixel elements characterized by a relativelylow degree of motion corruption; and (5) analyze the selected signal todetermine a vital sign value, e.g. cNIBP.

In embodiments, the multi-pixel detector features at least a 3×3 arrayof pixels, each containing a photodetector. In this case the opticalsensor is integrated with a circuit configured to de-multiplex signalsfrom the multi-pixel detector. The processor in the body-worn monitorcan be programmed to analyze the motion signal and a signal from eachpixel element to determine the signal that has the lowest correlation tothe motion signal, indicating that the signal is characterized by arelatively low degree of motion corruption. Correlation, for example,can be determined using standard algorithms known in the art, such asalgorithms that determine cross-correlation between two sequences ofdata points. Such algorithms can yield a Gaussian-type waveform, withthe amplitude of the waveform increasing with correlation. The waveformcan then be compared to a series of metrics to determine a numericalfigure of merit indicating the degree of correlation. Alternatively, theprocessor is programmed to analyze the motion signal to determine ameasurement period when patient movement is relatively low, and thenmeasure a signal from each pixel element. In both cases, the signal fromeach pixel element represents a PPG waveform featuring a sequence ofpulses, each characterized by an onset point. When combined with an ECGQRS complex, this waveform can yield a PTT as described above. Inembodiments the multi-pixel detector is included in the thumb-wornsensor described in detail below. Alternatively, it is incorporated in aflexible patch configured to be worn on the patient's forehead. In thiscase the flexible patch connects to a body-worn transceiver that issimilar to the wrist-worn transceiver in both form and function.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show, respectively, schematic drawings indicating theComposite Method's pressure-dependent and pressure-free measurements;

FIGS. 2A and 2B show graphs of, respectively, PTT and the amplitude ofthe PPG waveform measured as a function of pressure;

FIG. 3A shows a graph of PTT measured as a function of ‘effective’ meanarterial blood pressure (MAP*(P)) determined using the CompositeMethod's pressure-dependent measurement;

FIG. 3B shows a graph of PTT measured as a function of mean arterialblood pressure (MAP) determined using a conventional blood pressuremeasurement of the prior art;

FIG. 4A shows a graph of PTT measured as a function of both MAP*(P)(measured during inflation using the Composite Method'spressure-dependent measurement) and MAP (measured for two separate bloodpressure values using oscillometry) for a single patient;

FIG. 4B shows a graph of PTT measured as a function of both MAP*(P)(measured during deflation using the Composite Method'spressure-dependent measurement) and MAP (measured for two separate bloodpressure values) for a single patient;

FIGS. 5A and 5B show graphs of, respectively, a time-dependent pressurewaveform measured during both inflation and deflation, and the samewaveform after being filtered with a digital bandpass filter;

FIG. 6 shows a graph of amplitudes corresponding to heartbeat-inducedpulses taken from the inflationary portion of the graph in FIG. 5B andplotted as a function of pressure applied to a patient's brachialartery;

FIG. 7A shows a graph of time-dependent ECG and PPG waveforms andmarkers associated with these waveforms used to determine PTT;

FIG. 7B shows a graph of the time-dependent PPG waveform of FIG. 7A (toptrace), the first derivative of the waveform (middle trace), and thesecond derivative of the PPG waveform (bottom trace);

FIG. 8 is a schematic drawing showing a sequence of pressure-dependentand pressure-free measurements made during the Composite Method;

FIG. 9 is a schematic drawing showing how, during a clinical trial, anindexing measurement is made from the patient's brachial artery, and areference measurement using an A-line is made from the patient's femoralartery;

FIG. 10 shows a graph of time-dependent SYS values measured with theComposite Method (black trace), a femoral A-line (dark gray trace), anda radial A-line (light gray trace);

FIG. 11 shows a graph of time-dependent SYS and DIA values measured withthe Composite Method (gray trace) and SYS and DIA measured with afemoral A-line;

FIG. 12 shows a graph of a histogram of standard deviation values forSYS (dark bars) and DIA (light bars) measured during a 23-subjectclinical trial;

FIG. 13 shows a bar graph of FDA standard values and statistics from the23-subject study calculated using an ANOVA and AVERAGE methodologiesfor, respectively, intra-subject BIAS and STDEV for SYS (upper and lowerleft-hand corners); and intra-subject BIAS and STDEV for DIA (upper andlower right-hand corners);

FIG. 14 shows a table of drift of the SYS and DIA measurements madeaccording to the Composite Method corresponding, respectively, to 4 and8-hour indexing periods;

FIG. 15 shows a graph of a time-dependent PPG waveform measured with andwithout motion using an IR LED (top trace), a RED LED (second trace),the waveform measured with the IR LED divided by the waveform measuredwith the RED LED (third trace), and the third trace processed with adigital bandpass filter (fourth trace);

FIG. 16 shows a graph of a time-dependent PPG waveform measured with andwithout motion using an IR LED (top trace), a RED LED (second trace),the waveform measured with the RED LED subtracted from the waveformmeasured with the IR LED (third trace), and the third trace processedwith a digital bandpass filter (fourth trace);

FIG. 17 shows a graph of a time-dependent PPG waveform measured with andwithout motion using an IR LED and processed with a digital bandpassfilter (top trace), a RED LED and processed with a digital bandpassfilter (second trace), and the second trace subtracted from the firsttrace (third trace);

FIG. 18 shows a schematic drawing indicating an algorithm that allowscNIBP measurements to be made in both the presence and absence ofmotion;

FIG. 19 shows a graph of time-dependent PTT and HR measurements, and howthese can be processed with the algorithm shown in FIG. 18 to measurecNIBP in presence of motion;

FIGS. 20A and 21A show, respectively, time-dependent SYS waveforms madeusing a femoral A-line (dark gray) and reconstructed using the algorithmshown in FIGS. 18 and 19 to yield the best and worst results for the 23clinical subjects;

FIGS. 20B and 21B show, respectively, correlation plots generated usingdata from FIGS. 20A and 21A;

FIG. 22 shows a schematic view of a patient and a coordinate axis usedwith an algorithm and accelerometer waveforms to determine the patient'sposture;

FIG. 23A shows a graph of time-dependent accelerometer waveformsmeasured from a patient's chest during different postures;

FIG. 23B shows a graph of time-dependent postures determined byprocessing the accelerometer waveforms of FIG. 23A with an algorithm andthe coordinate axis shown in FIG. 22;

FIGS. 24A and 24B show, respectively, a three-dimensional image of thebody-worn monitor of the invention attached to a patient during andafter an initial indexing measurement;

FIG. 25 shows a three-dimensional image of the wrist-worn transceiverused with the body-worn monitor of FIGS. 24A and 24B;

FIG. 26 shows an image of a patient wearing a head-mounted sensorfeaturing a multi-pixel array photodetector for measuring a PPG waveformaccording to an alternate embodiment of the invention;

FIG. 27 shows a plan view of the multi-pixel array photodetector of FIG.26;

FIGS. 28A and 28B show a bolus of blood passing through a detecting areaof a conventional single-pixel photodetector for measuring a PPGwaveform;

FIGS. 29A and 29B show a bolus of blood passing through a detecting areaof the multi-pixel array photodetector of FIGS. 26 and 27; and

FIG. 30 shows a flow chart for measuring cNIBP, SpO2, respiration rate,heart rate, temperature, and motion according to the invention.

DETAILED DESCRIPTION OF THE INVENTION Theory of the Composite Method

FIGS. 1A and 1B show schematic drawings of the Composite Method'spressure-free (FIG. 1A) and pressure-dependent (FIG. 1B) measurements.Working in concert, these measurements accurately determine thepatient's cNIBP for an extended time without requiring an externalcalibration device, e.g., a conventional blood pressure cuff. During ameasurement, the patient wears a body-worn monitor attached to adisposable cuff and collection of optical, electrical, motion, andtemperature sensors. These sensors measure signals for both thepressure-dependent and pressure-free measurements. The co-pending patentapplications, the contents of which are fully incorporated herein byreference, describe earlier embodiments of this measurement: DEVICE ANDMETHOD FOR DETERMINING BLOOD PRESSURE USING ‘HYBRID’ PULSE TRANSIT TIMEMEASUREMENT (U.S. Ser. No. 60/943,464; filed Jun. 12, 2007); VITAL SIGNMONITOR FOR CUFFLESSLY MEASURING BLOOD PRESSURE USING A PULSE TRANSITTIME CORRECTED FOR VASCULAR INDEX (U.S. Ser. No. 60/943,523; filed Jun.12, 2007); and VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USINGOPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS (U.S. Ser. No. 12/138,194;filed Jun. 12, 2008). A microprocessor in the body-worn monitorprocesses the PPG and ECG waveforms to determine PTT, which is used inboth measurements of the Composite Method to determine cNIBP, as isdescribed in more detail below.

The cuff includes an air bladder which, when pressurized with apneumatic system, applies a pressure 107 to an underlying artery 102,102′. An electrical system featuring at least 3 electrodes coupled to anamplifier/filter circuit within cabling attached to the wrist-worntransceiver measures an ECG waveform 104, 104′ from the patient. Threeelectrodes (two detecting positive and negative signals, and one servingas a ground) are typically required to detect the necessary signals togenerate an ECG waveform with an adequate signal-to-noise ratio. At thesame time, an optical system featuring a transmissive or, optionally,reflective optical sensor measures a PPG waveform 105, 105′ featuring aseries of ‘pulses’, each characterized by an amplitude of AMP_(1/2),from the patient's artery. The preferred measurement site is typicallynear small arteries in the patient's thumb, such as the princepspollicis artery. A microprocessor and analog-to-digital converter withinthe wrist-worn transceiver detects and analyzes the ECG 104, 104′ andPPG 105, 105′ waveforms to determine both PTT₁ (from the pressure-freemeasurement) and PTT₂ (from the pressure-dependent measurement).Typically the microprocessor determines both PTT₁ and PTT₂ bycalculating the time difference between the peak of the QRS complex inthe ECG waveform 104, 104′ and the foot (i.e. onset) of the PPG waveform105, 105′.

The invention is based on the discovery that an applied pressure(indicated by arrow 107) during the pressure-dependent measurementaffects blood flow (indicated by arrows 103, 103′) in the underlyingartery 102, 102′. Specifically, the applied pressure has no affect oneither PTT₂ or AMP₂ when it is less than a diastolic pressure within theartery 102, 102′. When the applied pressure 107 reaches the diastolicpressure it begins to compress the artery, thus reducing blood flow andthe effective internal pressure. This causes PTT₂ to systematicallyincrease relative to PTT₁, and AMP₂ to systematically decrease relativeto AMP₁. PTT₂ increases and AMP₂ decreases (typically in a linearmanner) as the applied pressure 107 approaches the systolic bloodpressure within the artery 102, 102′. When the applied pressure 107reaches the systolic blood pressure, AMP₂ is completely eliminated andPTT₂ consequently becomes immeasurable.

During a measurement the patient's heart generates electrical impulsesthat pass through the body near the speed of light. These impulsesaccompany each heartbeat, which then generates a pressure wave thatpropagates through the patient's vasculature at a significantly slowerspeed. Immediately after the heartbeat, the pressure wave leaves theheart and aorta, passes through the subclavian artery, to the brachialartery, and from there through the radial and ulnar arteries to smallerarteries in the patient's fingers. Three disposable electrodes locatedon the patient's chest measure unique electrical signals which pass toan amplifier/filter circuit within the body-worn monitor. Typically,these electrodes attach to the patient's chest in a 1-vector‘Einthoven's triangle’ configuration to measure unique electricalsignals. Within the body-worn monitor, the signals are processed usingthe amplifier/filter circuit to determine an analog electrical signal,which is digitized with an analog-to-digital converter to form the ECGwaveform and then stored in memory. The optical sensor typicallyoperates in a transmission-mode geometry, and includes an optical modulefeaturing an integrated photodetector, amplifier, and pair of lightsources operating at red (˜660 nm) and infrared (˜905 nm) wavelengths.These wavelengths are selected because they are effective at measuringPPG waveforms with high signal-to-noise ratios that can additionally beprocessed to determine SpO2. In alternative embodiments, an opticalsensor operating in a reflection-mode geometry using green (˜570 nm)wavelengths can be used in place of the transmission-mode sensor. Such asensor has the advantage that it can be used at virtually any locationon the patient's body. The green wavelength is chosen because it isparticularly sensitive to volumetric absorbance changes in an underlyingartery for a wide variety of skin types when deployed in areflection-mode geometry, as described in the following co-pendingpatent application, the entire contents of which are incorporated hereinby reference: SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULEFEATURING A GREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3,2006).

The optical sensor detects optical radiation modulated by theheartbeat-induced pressure wave, which is further processed with asecond amplifier/filter circuit within the wrist-worn transceiver. Thisresults in the PPG waveform, which, as described above, includes aseries of pulses, each corresponding to an individual heartbeat.Likewise, the ECG waveforms from each measurement feature a series ofsharp, ‘QRS’ complexes corresponding to each heartbeat. As describedabove, pressure has a strong impact on amplitudes of pulses in the PPGwaveform during the pressure-dependent measurement, but has basically noimpact on the amplitudes of QRS complexes in the corresponding ECGwaveform. These waveforms are processed as described below to determineblood pressure.

The Composite Method performs an indexing measurement once every 4-8hours using inflation-based oscillometry. During the indexingmeasurement, a linear regression model is used to relate the pressureapplied by the cuff to an ‘effective MAP’ (referred to as MAP*(P) inFIG. 3A) representing a mean pressure in the patient's arm. MAP*(P) andthe PTT value associated with it vary tremendously during aninflationary process. As shown in FIG. 3A, this results in a unique setof MAP*(P)/PTT paired data points which can be extracted for eachheartbeat occurring as the applied pressure ramps from DIA to SYS. Thismeans calibration can be performed with a single, inflation-basedmeasurement that typically takes between 40-60 seconds. At a recommendedinflation rate (approximately 3-10 mmHg/second, and most preferablyabout 5 mmHg/second) this typically yields between 5-15 data points.These are the data points analyzed with the linear regression model todetermine the patient-specific slope. Blood pressure values(SYS_(INDEX), MAP_(INDEX), and DIA_(INDEX)) and the ratios between them(R_(SYS)=SYS_(INDEX)/MAP_(INDEX); R_(DIA)=DIA_(INDEX)/MAP_(INDEX))determined during the inflation-based measurement are also used in thiscalculation, and then for subsequent pressure-free measurements.

A stable PTT value is required for accurate indexing, and thus PTT ismeasured from both the ECG and PPG waveforms for each heartbeat overseveral 20-second periods prior to inflating the pump in the pneumaticsystem. The PTT values are considered to be stable, and suitable for theindexing measurement, when the standard deviation of the average PTTvalues from at least three 20-second periods (PTT_(STDEV)) divided bytheir mean (PTT_(MEAN)) is less than 7%, i.e.

$\begin{matrix}{\frac{{PTT}_{STDEV}}{{PTT}_{MEAN}} < 0.07} & (1)\end{matrix}$

When this criterion is met the pump is automatically inflated, and thepatient-specific slope is then determined as described above. Thisprocess is typically repeated every 4-8 hours. Once determined, theslope is analyzed with a series of empirical metrics to ensure that itis both realistic and consistent with those determined with previoustrials. An unrealistic personal slope would result, for example, if amotion-related artifact occurred during the indexing measurement. Ifeither the value or the linear fit used to determine it fails to meetthese metrics, then a default slope, determined from analyzing arterialline data collected from a large number of patients, is used in itsplace. Additionally, the above-described model tends to yield relativelyinaccurate results for patients with very low slopes (i.e., slopes lessthan −0.22 mmHg/ms), and for this case a secondary model is thereforeused. This model, which is typically determined experimentally onpatients having particularly low personal slopes, relates the personalslope to pulse pressure.

During an actual pressure-dependent indexing measurement, the body-wornmonitor collects data like that shown in FIGS. 2A and 2B, for anindividual patient. During a measurement, the microprocessor analyzesthe variation between applied pressure and PTT, shown graphically inFIG. 2A, to estimate the relationship between blood pressure and PTT. Asshown in Equation (2), below, this relationship is best described with amathematical model that first estimates how the patient's ‘effective’mean arterial blood pressure (MAP*(P)) varies with applied pressure(P_(applied)). The model assumes that pressure applied by the cuffoccludes the patient's brachial artery, and thus temporarily decreasesblood flow. This, in turn, increases blood pressure directly underneaththe cuff, and reduces blood pressure in the downstream radial, ulnar,and finger arteries. The net effect is a temporary, pressure-dependentreduction in the patient's mean arterial blood pressure (MAP), indicatedin Equation (2) as ΔMAP(P), during the pressure-dependent measurement.An empirically determined factor (F) accounts for the ratio between theregion of increased blood pressure (underneath the cuff; approximately10 cm) and the larger region of decreased blood pressure (the length ofthe arm downstream from the cuff; approximately 50 cm). F is typicallybetween 0.6 and 0.9, and is preprogrammed into the algorithm prior tomeasurement.ΔMAP(P)=F×(P _(applied)−DIA_(INDEX))MAP*(P)=MAP_(INDEX)−ΔMAP(P)  (2)

Using Equation (2), paired values of PTT and MAP*(P) are determined foreach heartbeat as the applied pressure increases from DIA_(INDEX) toMAP_(INDEX). This approach yields multiple data points during a singlepressure-dependent measurement that can then be fit with a mathematicalfunction (e.g. a linear function) relating PTT to MAP. Typically theseparameters are inversely related, i.e. PTT gets shorter and bloodpressure increases. In typical embodiments, therefore, an inverse linearrelationship determined during the pressure-dependent indexingmeasurement is then used during subsequent pressure-free measurements toconvert the measured PTT into blood pressure values.

In Equation (2), the values for DIA_(INDEX) and MAP_(INDEX) aredetermined with an oscillometric blood pressure measurement duringinflation. SYS_(INDEX) can either be determined indirectly during theoscillometric blood pressure measurement, or directly by analyzing thepressure-dependent pulse amplitude in the PPG waveform. In thisembodiment, as shown in FIG. 2B, the pulse amplitude will graduallyreduce with applied pressure, and eventually disappears when thispressure is equal to SYS. A conventional peak-detecting algorithmrunning on the microprocessor can thus detect the onset of the opticalpulse amplitude shown in FIG. 2B to make a direct measurement ofsystolic blood pressure. Alternatively, a ‘fitting’ algorithm can modelthe systematic decrease in pulse amplitude with applied pressure with amathematical function (e.g. a linear function) to estimate systolicblood pressure.

FIGS. 3A and 3B show graphs of PTT as a function MAP*(P) (FIG. 3A) andMAP (FIG. 3B) for a single patient. Each data point 126, 129 in thegraphs includes error bars representing an approximate measurementerror. In FIG. 3A, the data points 126 are determined during a single,30-second pressure-dependent measurement of the Composite Method; eachdata point represents PTT and MAP*(P) values for an individualheartbeat. These data points are derived, for example, by combiningmeasurements similar to those shown in FIG. 2A (PTT as a function ofapplied pressure) and Equation (2) (MAP*(P) calculated from appliedpressure). In contrast, the two data points 129 in FIG. 3B are derivedby simply measuring PTT and MAP during separate blood pressuremeasurements. Each measurement normally takes about 60 seconds tocomplete; they are ideally done at separate points in time when thepatient's blood pressure (and corresponding PTT) differs by a measurableamount.

The two graphs illustrate the advantages of determining apatient-specific relationship between PTT and blood pressure during theComposite Method's pressure-dependent measurement. As shown in FIG. 3A,the data points 126 vary over approximately a relatively large range inblood pressure (typically 15 mmHg or more); they are typically tightlycorrelated, and, despite any measurement error, can be easily fit with asingle linear equation (y=Mx+B) shown by the dashed line 125. Incontrast, if the patient's blood pressure is relatively stable, the twodata points 129 of FIG. 3B can have similar values, even if they aremeasured several hours apart. These two values can yield fits withdifferent linear equations (y=M₁x+B₁ and y=M₂x+B₂ and) even when themeasurement error is low. Using an inaccurate linear equation in thisinstance can, in turn, result in an inaccurate relationship between PTTand blood pressure. Ultimately this adds error to the PTT-based bloodpressure measurement.

FIGS. 4A and 4B show actual PTT vs. MAP*(P) and MAP data, measured for asingle patient, during a pressure-dependent measurement that usesinflation (FIG. 4A) and deflation (FIG. 4B). In the figures thetriangles indicate PTT vs. MAP*(P) determined during the CompositeMethod's pressure-dependent indexing measurement. These data represent acalibration of the blood pressure measurement. The squares indicatesubsequent, measurements wherein MAP is determined using an automatedblood pressure cuff, and PTT is determined using the body-worn monitordescribed herein. As is clear from the figures, the values of PTT vs.MAP*(P) measured during inflation (FIG. 4A) have a tight,well-correlated distribution compared to those measured during deflation(FIG. 4B). This indicates that a calibration determined from apressure-dependent measurement made during inflation is likely moreaccurate than one made during deflation. Without being bound by anytheory, this discrepancy may be due an inflation-basedpressure-dependent measurement that gradually reduces blood flow in anunderlying artery until it is ultimately occluded. In contrast, adeflation-based measurement first fully occludes the artery, and thengradually reduces the occlusion as the cuff deflates. Dammed-up bloodrapidly flows through the artery during this process. This increase inblood flow may cause turbulence and other complicated hemodynamic eventsthat add variability to the PTT value. Such processes are likely notpresent during an inflation-based measurement.

In FIG. 4A, a linear fit to the values of PTT vs. MAP*(P), shown by thedashed line 130, also fits the measurements of PTT vs. MAP. Thisindicates a calibration determined during the pressure-dependentmeasurement (triangles) can be used to accurately measure blood pressurevalues made during subsequent pressure-free measurements (squares). InFIG. 4B, the linear fit to the PTT vs. MAP*(P) values, shown by thedashed line 131, does not accurately fit the measurements of PTT vs.MAP. This result is expected based on the variability of the PTT vs.MAP*(P) values, and indicates that this calibration has a relatively lowaccuracy compared to that made during inflation.

Use of Inflation-Based Oscillometry in the Composite Method

FIG. 5A illustrates the equivalency between inflation-based anddeflation-based oscillometric blood pressure measurements. The topportion of the figure shows an unfiltered pressure waveform 139,measured during the pressure-dependent measurement, which includesperiods of both inflation 137 and deflation 138. Pulses associated withthe patient's heartbeat couple into a bladder in the cuff during bothperiods. Following a measurement, the pressure waveform 139 is processedusing a 0.5→5.0 Hz digital bandpass filter to remove the slowly varyingbaseline. As shown in FIG. 5B, filtering results in a time-dependentpressure waveform 140 featuring separate pulse trains measured duringboth inflation and deflation; the time-dependent amplitudes of eachpulse in the train are characterized by a Gaussian envelope. Pressurecorresponding to the peak of the Gaussian envelope represents a directmeasurement of mean arterial pressure. Diastolic blood pressure, whichis measured indirectly, corresponds to a pressure less than meanarterial pressure when the ratio of the envelope to its maximum value is0.72. This ratio, along with the ratio for systolic blood pressure(typically 0.55), is described in more detail in U.S. Pat. No.6,719,703, the contents of which are incorporated herein by reference.

As described above, oscillometry is used during the indexing measurementto determine SYS_(INDEX), DIA_(INDEX), and MAP_(INDEX). These values areextracted from a ‘processed pressure waveform’, shown in FIG. 6, whichis determined from a pressure waveform collected during inflation asshown in FIG. 5. The pressure waveform indicates how amplitude of eachheartbeat-induced pulse in the time-dependent pressure waveform varieswith pressure applied by the cuff. During a measurement, a pressuresensor in the pneumatic system shown in FIG. 24A collects and digitizesthe pressure waveform, which is then processed as described below todetermine the processed pressure waveform, and ultimately SYS_(INDEX),DIA_(INDEX), and MAP_(INDEX).

A two-stage digital filtering algorithm determines the processedpressure waveform. This involves first filtering the raw pressurewaveform with a bandpass filter that, in typical applications, featuresa second-order infinite impulse response (IIR) function that passesfrequencies between 0.5→7.5 Hz. The second-order IIR filter transferfunction typically takes the form:

$\begin{matrix}{{H_{F}(z)} = \frac{{b_{0}z^{2}} + {b_{1}z} + b_{2}}{z^{2} + {a_{1}z} + a_{2}}} & (3)\end{matrix}$and is implemented as a difference equation, as shown in Equation (4):y[n]=b ₀ x[n]+b ₁ x[n−1]+b ₂ x[n−2]−a ₁ y[n−1]−a ₂ y[n−2]  (4)

Input to the first stage of the IIR filter is the raw, unprocessedpressure waveform, similar to that shown in FIG. 5A. Processing with thefirst stage yields the pulse waveform, similar to that shown in FIG. 5B.In order to remove any phase distortion, the IIR filter is executed inboth the forward and reverse directions. The reverse filtering stepdoubles the effective order of the filter, and cancels out any phasedistortion introduced by the forward filtering operation. The reversefiltering step is implemented by executing the standard IIR differenceequation (i.e. Equation (4)), performing a time-reversal on theoutputted data, and then executing the same IIR difference equation.While effective in removing phase distortion, such additional stepsrequire an extra difference computation which cannot be performed inreal-time on a stream of data. This, in turn, increases powerconsumption in the wrist-worn transceiver, and thus shortens batterylife.

As the cuff inflates around the patient's arm, perturbations due topatient motion, kinks in the cuff, rapid speed changes in the pump'smotor, and other artifacts may affect the pressure waveform. Suchperturbations are typically non-physiological, and thus should beremoved to minimize their influence on the oscillometric envelope. Theirimpact can be minimized by a number of different techniques. Theseinclude setting certain, noise-containing sections of the pressurewaveform equal to zero and removing any data points in the waveform thatshow a rapid change in value over a relatively short period of time.After the potential artifacts have been removed, the pulse waveform isrectified to prepare for the second filtering operation. Rectificationinvolves transforming the waveform into a new waveform (P_(RECT)) thatfeatures only positive components. P_(RECT) is calculated from theoriginal pressure waveform (P_(ORIG)) using Equation (5), below:

$\begin{matrix}{{P_{RECT}(i)} = \{ \begin{matrix}{{- 1} \times {P_{ORIG}(i)}} & {{{if}\mspace{14mu}{P_{ORIG}(i)}} < 0} \\{P_{ORIG}(i)} & {otherwise}\end{matrix} } & (5)\end{matrix}$

To complete the second phase of the filtering process, the rectifiedwaveform is filtered with a digital low-pass filter based on an IIRfilter. The low-pass filter typically only passes components less than0.2 Hz to yield a smooth, low-frequency envelope indicating the pulseamplitude variation, as shown in FIG. 6. This waveform represents the‘processed pressure waveform’, and can then be analyzed with techniquesborrowed from oscillometry to determine the patient's ‘indexed’ bloodpressure values, i.e. SYS_(INDEX), DIA_(INDEX), and MAP_(INDEX).Specifically, the peak of the processed pressure waveform corresponds toMAP_(INDEX). This is because, during oscillometry, the maximum amplitudeof the heartbeat-induced pulses occurs when the brachial transmuralpressure is zero. This takes place when the pressure inside the cuffequals MAP in the brachial artery. Oscillometry thus represents a directmeasure of MAP. Both SYS_(INDEX) and DIA_(INDEX) are calculated using anempirical model based on amplitudes of the waveform on both sides ofMAP_(INDEX), as indicated in FIG. 6. During an actual measurement, thepeak of the processed pressure waveform is determined using standardmeans, such as calculating a mathematical derivative and determining apositive-to-negative zero-point crossing. SYS_(INDEX) and DIA_(INDEX)are then determined from features of the waveform located, respectively,at higher and lower pressures compared to MAP_(INDEX). Referring againto FIG. 6, SYS_(INDEX), for example, is the pressure corresponding to0.55 times the peak amplitude on the right-hand (high-pressure) side ofthe processed pressure waveform. DIA_(INDEX) is the pressurecorresponding to 0.70 times the peak amplitude on the left-hand (lowpressure) side of the waveform.

The above-described ratios (0.55 and 0.70) corresponding to SYS_(INDEX)and DIA_(INDEX) are typically determined empirically using studies witha large and diverse patient population. They can vary with physiologicalproperties associated with a given patient. For example, the ratios canvary depending on the patient's MAP, shape of the processed waveform,heart rate, biometric data (e.g. gender, height, weight, age), and otherfactors. A reference that describes the variation of ratios with theshape of the processed pressure waveform is described in the followingreference, the contents of which are fully incorporated herein byreference: Amoore et al., ‘Effect of the shapes of the pulse amplitudeoscillometric envelope and their characteristic ratios on thedifferences between auscultatory and oscillometric blood pressuremeasurements’, Blood Pressure Monitoring 2007; 12:297-305. Oncedetermined, the resultant values for MAP_(INDEX), SYS_(INDEX), andDIA_(INDEX) can be checked for accuracy using a variety of simple tests.For example, MAP_(INDEX) can be compared to the geometric MAP(MAP_(GEO)) determined from SYS_(INDEX) and DIA_(INDEX) using Equation(6), below. This test is based on the inherent relationship between MAP,SYS, and DIA, as described in the following reference, the contents ofwhich are fully incorporated herein by reference: Chemla et al., ‘Meanaortic pressure is the geometric mean of systolic and diastolic pressurein resting humans’, J Appl Physiol 2005; 99:2278-2284.|MAP_(DIFF)|>DIFF_(MAX), where MAP_(DIFF)=(MAP_(INDEX)−MAP_(GEO))  (6)

In Equation (6) MAP_(GEO) is determined from the following equation:MAP_(GEO)=√{square root over ((SYS_(INDEX)×DIA_(INDEX)))}  (7)

In embodiments, for example, DIFF_(MAX) is equal to 13 mmHg. This meansa measurement is rejected if the difference between MAP_(INDEX) andMAP_(GEO) is greater or less than 13 mmHg. Such a situation would occur,for example, if the processed pressure waveform was distorted by amotion-related artifact that occurred during the oscillometricmeasurement. When an oscillometric measurement is rejected, a NULL valueis returned, and the body-worn monitor instructs the pneumatic system tore-inflate the cuff, and the measurement is repeated.

Once MAP_(INDEX), SYS_(INDEX), and DIA_(INDEX) are determined, thesystolic and diastolic ratios (R_(SYS) and R_(DIA)) are calculated asdescribed below in Equation (8):R _(SYS)=SYS_(INDEX)/MAP_(INDEX)R _(DIA)=DIA_(INDEX)/MAP_(INDEX)  (8)

These ratios may vary in a dynamic fashion according to otherphysiological parameters determined during a measurement, particularlyheart rate. Such variation is described in the above-referenced journalarticle, entitled Chemla et al., ‘Mean aortic pressure is the geometricmean of systolic and diastolic pressure in resting humans’, J ApplPhysiol 2005; 99:2278-2284, the contents of which have been previouslyincorporated by reference. For example, Equation (9), below, indicateshow these ratios may vary with heart rate:R _(SYS) =a×HR×SYS_(INDEX)/MAP_(INDEX)R _(DIA) =b×HR×DIA_(INDEX)/MAP_(INDEX)  (9)

In Equation (9), the coefficients a and b are determined empirically,typically using studies on either humans or animals. For these studiesblood pressure and heart rate data are typically collected with adiverse group of patients undergoing a range of physiologicalconditions, and then analyzed. Note that the ratios shown in Equation(9) will only exhibit dynamic behavior if the patient's heart rate isvariable.

As described above, the Composite Method can also include anintermediate pressure-dependent indexing measurement that determinessystolic, diastolic, and means arterial pressures using an abbreviatedapplied pressure. In this case, to find systolic blood pressure, thealgorithm can detect the amplitude of each pulse in the PPG waveform,and fit them to a variety of mathematical models to ‘predict’ andextrapolate exactly where the amplitude decreases to zero. For example,the algorithm can fit the last eight data points in FIG. 4B to a linearfunction. In this case knowledge of the patient's heart rate (e.g.frequency and rhythm), as determined from the ECG waveform, can enhancethe accuracy of the prediction and provide a confidence indicator of themetric. The algorithm may take a mathematical derivative of the PPGwaveform to eliminate any affects of the waveform's baseline. Theabove-described algorithms may then be used to predict disappearance ofthe pulse and thus the onset of systolic blood pressure.

During the intermediate pressure-dependent measurement, pressure istypically applied until just after mean arterial pressure is calculatedas described above, and then terminated. At this point, the amplitude ofthe PPG waveform is typically in decline, and can be fit with the linearfunction to predict systolic blood pressure. Both systolic and meanarterial pressures are then used to determine diastolic pressure, asdescribed above. The intermediate pressure-dependent measurement istypically performed, for example, every 4 hours in place of the regularpressure-dependent measurement.

Measuring PTT and Determining cNIBP with the Composite Method

Following indexing, cNIBP is determined on a beat-by-beat basis fromPTT, which as indicated by the arrow 154 in FIG. 7A is determined fromthe time difference between features in the ECG and PPG waveforms.Specifically, PTT separates a sharply peaked QRS complex in the ECGwaveform, indicated in the figure by the black circle 150, from the baseof the PPG waveform, shown by the black circle 151. PTT typically variesinversely with blood pressure, i.e. a decrease in PTT indicates anincrease in blood pressure. In theory, PTT is affected by blood pressureand a variety of other factors, such as arterial compliance, arterialsize, vascular resistance, PEP, and LVET. For this reason, PTT, taken byitself, only indicates relative changes in blood pressure. But whencombined with the above-mentioned indexing process, which estimatesabsolute blood pressure values and ‘calibrates’ for factors that affectPTT but not necessarily blood pressure, PTT can accurately monitorcNIBP. As described above, during a measurement the body-worn monitormeasures PTT corresponding to every heartbeat for a given time period,typically lying between 20-60 seconds. During this time period, specificPTT values may be filtered out to remove erroneous values affected byartifacts, such as motion. For example, both average and standarddeviation values can be calculated for a set of PTT values measuredduring the time period. The total number of PTT values will, of course,depend on the heart rate, and is typically between 15 and 60 for a30-second measurement period. Values that differ from the average bymore than one standard deviation can be assumed to be artificial, andthus removed from the calculation. At this point an average PTT value isthen recalculated for the time period and used for the subsequent cNIBPcalculation. Similar statistical processing techniques, such as thoseusing numerical fitting, processing of Gaussian distributions, ordigital filtering, can also be used to exclude PTT values estimated tobe erroneous. Statistics are typically calculated for individual timeperiods. Alternatively, they may be calculated on a ‘rolling basis’ inwhich the time period is kept relatively large, but is sequentiallyupdated, e.g., each second. This approach has the advantage that it canyield a ‘fresh’ blood pressure value at a relatively high frequency.

Referring again to FIG. 7A, PTT is typically calculated from the foot or‘onset’ of the PPG waveform, indicated by the black circle 151, whichindicates an arrival of the pressure pulse. Physically, the onset point151 represents beginning of a volumetric increase in vasculature thatlies underneath the thumb-worn sensor (294) shown in FIG. 24A. Apressure pulse launched by the patient's beating heart propagates alongtheir vasculature, driving blood into it and causing a temporaryexpansion upon its arrival. The expansion increases optical absorptionaccording to the Beer-Lambert law. Radiation that passes through theexpanding vasculature is detected by a photodetector, resulting in atime-dependent PPG. Technically, the waveform shown in FIG. 7A is aninverted version of the ‘true’ PPG, as the increase in opticalabsorption reduces the amount of radiation and resulting signal detectedby the photodetector within the thumb-worn sensor.

Alternatively, PTT can be calculated from other regions of the waveform,such as a point along its rising edge or its peak. Timing associatedwith these regions, however, may be affected by properties of theunderlying vasculature (such as elasticity) that are decoupled fromblood pressure. For this reason they are less desirable than thewaveform's onset. In embodiments, however, they may be used to augmentcalculation of PTT. For example, as shown by the middle trace of FIG.7B, the first derivative of the PPG yields a well-defined peakindicating the maximum slope of the PPG that can easily be detected witha computer algorithm. For unusually noisy PPGs, this fiducial marker maybe used to help locate the PPG's onset, or may be processed with theonset to generate an ‘average’ PTT value for the waveform. Otherfeatures of the waveform, such as its maximum value, may also beprocessed in a similar manner.

In other embodiments, multiple PPGs measured during a SpO2 measurementmay be processed to generate a single PTT. Such a measurement isdescribed in the following co-pending patent application, the contentsof which are fully incorporated herein by reference: ‘BODY-WORN PULSEOXIMETER’ (U.S. Ser. No. 12/559,403; filed Sep. 14, 2009). As describedin this reference, during a typical SpO2 measurement PPGs are measuredwith both red (˜660 nm) and infrared (˜905 nm) wavelengths. These PPGshave similar features, but may be affected by motion-related noise, aswell as other artifacts such as ambient light, in different ways. Theonset of each PPG can thus be independently detected, and then averagedtogether to generate a single PTT. Other techniques for processingmultiple PPGs to determine a single PTT are described below,particularly with reference to FIGS. 15-17.

FIG. 7B shows one method for determining the onset of a PPG waveform,indicated in the top portion of the figure by the black circle 152.Before processing, the PPG waveform is typically filtered with a digitalfinite impulse response (FIR) filter, which removes high-frequency noisefrom the waveform prior to processing. Such noise is typically due toelectrical or mechanical sources. Removing it is critical for effectivesignal processing, as it is amplified after taking a numericalderivative. This reduces a signal-to-noise ratio of the derivatizedwaveform, which in turn may lead to erroneous measurements. The firstderivative of the PPG waveform peaks at a point corresponding to themaximum rise time of the unprocessed PPG waveform. This point, shown inthe middle trace of FIG. 7B, typically follows the onset point by 20-100ms. As shown as the bottom trace in the figure, the second derivative ofthe waveform peaks at a point corresponding to the onset. This isindicated in the figure by the black circle 153, and correlates with thePPG onset as indicated by the dashed line 155. Such a peak ischaracterized by a well-defined positive-to-negative slope change, andis relatively easy to detect with a standard computer algorithm. Oncedetected, this value is processed along with the ECG QRS to determinePTT.

Once determined, PTT is used along with blood pressures determinedduring indexing with inflation-based oscillometry (MAP_(INDEX),SYS_(INDEX), and DIA_(INDEX)) and a patient-specific slope (m_(cNIBP))to determine a MAP component of cNIBP (MAP_(cNIBP)). Equation (10),below, shows the relationship between these parameters:MAP_(cNIBP)=(m _(cNIBP)×PTT)−(m _(cNIBP)×PTT_(INDEX))+MAP_(INDEX)  (10)where PTT_(INDEX) is the PTT value determined at the start of theindexing process. SYS_(cNIBP) and DIA_(cNIBP) are then determined fromMAP_(cNIBP) for each heartbeat using the relationships described inEquation (11), below:SYS_(cNIBP)=MAP_(cNIBP) ×R _(SYS)DIA_(cNIBP)=MAP_(cNIBP) ×R _(DIA)  (11)where R_(sys) and R_(DIA) are described above in Equation (8) and,optionally, Equation (9).

In other embodiments, the blood pressure ratios shown in Equation (11)can be adjusted depending on other signals measured from the patient,such shapes associated with the PPG and ECG waveforms. For example, arelationship between the PPG waveform shape and SYS, DIA, and MAP thatcan be used in this embodiment is described in U.S. Pat. No. 5,269,310,the contents of which are incorporated herein by reference. In otherembodiments, unique patient-specific slopes and y-intercepts relatingSYS, DIA, and MAP to PTT, similar to that shown for MAP_(cNIBP) inEquation (10), can be determined beforehand and used to independentlycalculate these blood pressures. In still other embodiments, ‘default’slopes calculated beforehand from large groups of patients can be usedin place of the patient-specific slopes. A default slope would be used,for example, if it were difficult to determine a patient-specific slopeas described above because of a motion-related artifact or a problemassociated with the pneumatic system.

Implementation of the Composite Method

FIG. 8 shows one possible sequence 178 of the Composite Method'spressure-dependent (steps 182 a), pressure-free (steps 181 a, 181 b, 181c), and intermediate pressure-dependent (steps 182 b, 182 c)measurements for a patient undergoing an extended hospital stay. Duringthe stay, a medical professional applies the body-worn monitor, opticalsensor, and chest electrodes to the patient (step 180). This takes aboutone minute. The medical professional may also collect biometricinformation from the patient, such as their age, weight, height, gender,ethnicity, and whether they are on blood pressure medications, and enterthese into the monitor using a graphical user interface and touch panel.This information is then communicated wirelessly to the remote monitor.Going forward, a microprocessor within the body-worn monitor'selectronics module first initiates a pressure-free measurement (step 181a) for about one minute, wherein the body-worn monitor collects PPG andECG waveforms from the patient, determines their heart rate and PTT, andcan estimate their blood pressure. In the absence of an absolute bloodpressure measurement from the Composite Method's pressure-dependentmeasurement, the microprocessor may use PTT and the patient's biometricinformation to estimate blood pressure, as is described in the followingco-pending patent application, the contents of which have beenpreviously incorporated herein by reference: DEVICE AND METHOD FORDETERMINING BLOOD PRESSURE USING ‘HYBRID’ PULSE TRANSIT TIME MEASUREMENT(U.S. Ser. No. 60/943,464; filed Jun. 12, 2007); and, VITAL SIGN MONITORFOR CUFFLESSLY MEASURING BLOOD PRESSURE USING A PULSE TRANSIT TIMECORRECTED FOR VASCULAR INDEX (U.S. Ser. No. 60/943,523; filed Jun. 12,2007). This process typically determines systolic and diastolic bloodpressure with an accuracy of about ±10-15 mmHg.

The initial, approximate value for the patient's blood pressure andheart rate determined during the first pressure-free measurement (step181 a) can then be used to set certain parameters during the followingfirst pressure-dependent indexing measurement (step 182 a). Knowledge ofthese parameters may ultimately increase the accuracy of the firstpressure-dependent measurement (step 182 a). Such parameters, forexample, may include inflation time and rate, fitting parameters fordetermining the time-dependent increase in PTT and the time-dependentdecrease in PPG waveform amplitude during the pressure-dependentmeasurement. Of particular importance is an accurate value of thepatient's heart rate determined during the first pressure-freemeasurement (step 181 a). Since both PTT and amplitude can only bemeasured from a pulse induced by a heartbeat, the algorithm can processheart rate and use it in the fitting process to accurately determine thepressure at which the PPG waveform amplitude crosses zero.

Using parameters such as heart rate and initial estimated bloodpressure, the first pressure-dependent indexing measurement (step 182 a)determines a relationship between PTT and blood pressure as describedabove. This measurement takes about 40 seconds, and may occurautomatically (e.g., after about 1 minute), or may be driven by themedical professional (e.g., through a button press). The microprocessorthen uses this relationship and a measured value of PTT to determineblood pressure during the following pressure-free measurement (step 181b). This measurement step typically proceeds for a well-defined periodof time (e.g., 4-8 hours), during which it continuously determines bloodpressure. Typically, to conserve battery life, the body-worn monitoraverages PTT values over a 10-20 second period, and makes one bloodpressure measurement every 3-5 minutes.

The microprocessor may also perform a pre-programmed or automatedintermediate pressure-dependent measurement (step 182 b) to correct anydrift in the blood pressure measurement. As described above, this stepinvolves only partial inflation of the bladder within the cuff, duringwhich the microprocessor fits the pressure-dependent decrease in theamplitude of pulses in the PPG waveform to a linear model. Thismeasurement takes less time than the first pressure-dependentmeasurement (step 182 a), and accurately determines blood pressurevalues that are used going forward in a second pressure-free measurement(step 181 c). As before, this measurement typically continues for awell-defined period of time. At a later time, if the patient experiencesa sudden change in other vital signs (e.g., respiratory rate, heartrate, body temperature), the microprocessor may analyze this conditionand initiate another pressure-dependent blood pressure measurement (step182 c) to most accurately determine cNIBP.

Correlation Between cNIBP Measurements Made with the Composite Methodand a Femoral A-Line

cNIBP measurements made according to the Composite Method correlateparticularly well to blood pressure continuously measured from apatient's femoral artery using an arterial catheter, or ‘A-line’.Correlating cNIBP measurements to this reference standard represents animprovement over many previous studies that relate PTT to blood pressuremeasured with an A-line inserted a patient's radial artery, a locationthat is commonly used in hospital settings, such as the ICU. Suchstudies are described, for example, in the following references, thecontents of which are incorporated herein by reference: Payne et al.,‘Pulse transit time measured from the ECG: an unreliable marker ofbeat-to-beat blood pressure’, J Appl Physiol 2006; 100:136-141. Onereason for poor agreement between blood pressure measured with PTT and aradial A-line involves a phenomenon called ‘pulse pressureamplification’ wherein a patient's blood pressure gradually increasesalong their arterial tree as the diameter of the artery supporting thepressure is decreased, as described in the following reference, thecontents of which are fully incorporated herein by reference: Verbeke etal., ‘Non-invasive assessment of local pulse pressure: importance ofbrachial to radial pressure amplification’, Hypertension 2005;46:244-248. To summarize, gradual tapering that commonly occurs from thebrachial to radial arteries can have little effect on DIA or MAP, butcan increase pulse pressure (defined as SYS-DIA) by as much as 10 mmHgor more. For the measurement described herein, this means blood pressuremeasured at the radial artery is typically higher than that measured atthe brachial artery. And this phenomenon can reduce correlation betweenblood pressure measured using the Composite Method and a radial A-line,as the Composite Method is calibrated using an indexing measurement madeat the patient's brachial artery. In contrast, blood pressure at thefemoral artery is typically similar to that measured at the brachialartery. The following references, the contents of which are fullyincorporated herein by reference, describe the strong correlationsbetween blood pressures measured at these different sites: Park et al.,‘Direct blood pressure measurements in brachial and femoral arteries inchildren’, Circulation 1970; XLI:231-237; and Pascarelli et al.,‘Comparison of leg and arm blood pressures in aortic insufficiency: anappraisal of Hill's Sign’, Brit Med J 1965; 2:73-75. Without being boundto any theory, the strong correlation between brachial and femoralpressure may occur because both arteries are large, close to thepatient's heart, and support pressures indicative of the patient's core.The relatively large diameters of these arteries may additionallyminimize the influence of the arterial wall on the internal pressure. Incontrast, the radial artery is a significantly smaller artery with arelatively high surface-to-volume ratio, which tends to increase bloodpressure. This is one reason, for example, that SYS measured at apatient's extremities (using e.g. a finger cuff) is typically higherthan their core blood pressure.

FIG. 9 shows a graphic that this indicates this effect. In the figure, apatient 200 undergoing a cNIBP measurement has an indexing measurementperformed at their brachial artery, as indicated by the black circle201. As described above, the indexing measurement yields oscillometricblood pressures and a patient-specific relationship between PTT andblood pressure. These parameters are specific to the brachial artery,but also agree well with those determined at other large arteries, suchas the femoral arteries. PTT indicates a transit time describing thedelay associated with a pressure pulse launched by the patient'sheartbeat and arriving at the optical sensor, which is typically locatedon their thumb. The pathway associated with the transit time is shown bythe line 203 in the figure. During a correlation study, a referencemeasurement is typically made using an A-line inserted into thepatient's femoral artery, as shown by the black circle 202. Correlationbetween these two measurements is typically very good, presumablybecause the large nature of both the femoral and brachial arterieslimits the influence of pulse pressure amplification, particularly onSYS values.

In some cases, however, instantaneous blood pressure measured at boththe femoral and brachial arteries do not agree. According to theabove-described references (particularly Pascarelli et al.), differencesbetween these pressures may be as large as 20 mmHg, and typically resultfrom cardiac problems such as blockages or ‘aortic insufficiencies’.Differences tend to be larger for unhealthy patients. They typicallyaffect the average difference, or bias, between the cNIBP measurementdescribed herein and the reference A-line measurement, but have littleeffect on the correlation between these two measurements. If a bloodpressure study with a large number of patients is performed, differencesbetween femoral and brachial blood pressures may also contribute to aninter-subject (i.e. ‘between subject) error, typically characterized bya standard deviation. Such errors can be compensated for during thestudy with a calibration approach involving measuring brachial bloodpressure with a reference technique, such as manual auscultation, andusing this value to estimate the patient's inherent brachial-femoralblood pressure difference. In this case a calibration measurementindicates if disagreement between cNIBP and femoral A-line measurementsare caused by device-to-device measurement differences, or humanphysiology.

Clinical Results

FIG. 10 shows a typical, time-dependent cNIBP measurement according tothe Composite Method, and how this yields a SYS value that correlatesbetter with blood pressure measured at the femoral artery compared tothe radial artery. For this study, each of the above-mentioned bloodpressures (cNIBP, femoral, radial) were measured simultaneously. Allmeasurements were performed in the ICU of a hospital based in San Diego,Calif. Both femoral and radial pressures were measured every second within-dwelling A-line catheters connected to a conventional vital signmonitor (Philips Intellivue). cNIBP was simultaneously measured andaveraged over a 40-second period with a device similar to that shown inFIG. 24. Data from the Philips Intellivue monitor was sent through aserial connection to a specialized data-acquisition computer running acustom software application, while data from the cNIBP measurement wassent through a wireless connection (using Bluetooth) to the samecomputer. Once collected by the data-acquisition computer, bloodpressure values from the A-lines were averaged over an identical40-second period, and then compared to the cNIBP data. All data forthese experiments were collected over a 4-hour period with a singleindexing measurement performed at the beginning of the study.

As shown in FIG. 10, cNIBP measurements (black trace) agree fairly wellwith corresponding measurements from the femoral A-line (dark graytrace) over the entire 4-hour period, with both the STDEV (5.7 mmHg) andBIAS (−2.4 mmHg) between paired values of these measurements fallingwell below the FDA's standards for blood pressure monitoring devices(STDEV<8 mmHg; BIAS<+/−5 mmHg). These standards are described in detailin the AAMI SP-10:2002, a standards document that outlines requirementsfor blood pressure monitoring devices. As described above, particularlywith reference to FIG. 9, the cNIBP measurement is indexed with anoscillometric measurement made at the subject's brachial artery, arelatively large vessel that, like the femoral artery, supports apressure that is representative of core blood pressure. Larger arteriesnear the patient's core, unlike smaller arteries at the extremities,typically do not facilitate artificially elevated blood pressures due topulse pressure amplification. Blood pressure measured at the subject'srelatively small radial artery (light gray trace) is elevated comparedto both cNIBP and femoral blood pressures. Additionally, blood pressureat the radial artery tends to be relatively volatile compared to theother blood pressures. Without being bound to any theory, this too mayalso be due to pulse pressure amplification. Correlation between radialblood pressure and cNIBP is notably worse than that between cNIBP andfemoral blood pressure, with both STDEV (9.3 mmHg) and BIAS (−12.8 mmHg)for the paired values falling outside of the FDA's guidelines. Datasimilar to that shown in FIG. 10 was collected from 23 subjects in arecent study using the device and method according to the invention.

FIG. 11 shows typical correlation between both SYS and DIA measured withthe Composite Method and a femoral A-line. Data were collected in thesame manner described above. Blood pressure was measured over a 4-hourperiod with only a single indexing measurement performed at thebeginning of the study. cNIBP measurements for both SYS and DIA in FIG.11, like that described above in FIG. 10, correlate well with pressuresmeasured at the femoral artery, and comfortably meet the guidelinesrequired by the FDA. FIG. 12, for example, shows blood pressurecorrelations (shown as standard deviation) from a cohort of 23 subjectsmeasured in two different ICUs using both the Composite Method and afemoral A-line. All subjects were measured under essentially identicalconditions to those described above. The figure shows a histogram thatgraphs standard deviation values between the two measurements for bothSYS (dark gray bars) and DIA (light gray bars). The histogram indicatesthat, for all measurements collected from 23 subjects, only a singlemeasurement (for SYS) falls outside the FDA's guidelines of 8 mmHg forSTDEV. All other measurements are comfortably within this limit, and asexpected show Gaussian-type distributions, with the distributions peakednear 3 and 4 mmHg for, respectively, SYS and DIA.

FIG. 13 shows the collective, intra-subject statistics determined forall 23 subjects for the above-described study. Statistics weredetermined using two independent techniques. The first technique, called‘analysis of variance’ or ‘ANOVA’, tests statistical variance for a setof repeated measures, like those used in the ICU study. ANOVA models forSTDEV and BIAS are commonly used for analysis of data collected inclinical trials, like the one described herein. The second techniqueshown in FIG. 13 simply calculates the STDEV and BIAS for each subjectin the study over the 4-hour measurement period, and then calculates theaverage of each of these parameters for the group. In general, this‘AVERAGE’ analysis is less formal than an ANOVA analysis; it is usedherein simply to provide a secondary analysis of the data.

As shown in FIG. 13, for the 23 subjects measured in this study, theintra-subject cNIBP measurements analyzed with both ANOVA and theAVERAGE analysis comfortably meet the FDA's requirements for bothintra-subject BIAS and STDEV. For SYS, the intra-subject BIAS wascalculated at 2.1 and 2.6 mmHg using, respectively, the ANOVA andAVERAGE analyses. Intra-subject STDEVs for SYS were calculated as 5.9and 4.8 mmHg using the two techniques. For DIA, the intra-subject BIASwas calculated at 1.9 and 2.7 mmHg using, respectively, the ANOVA andAVERAGE analyses. Intra-subject STDEVs for DIA were calculated as 3.6and 3.0 mmHg using the two techniques. In all cases, these statisticscomfortably meet the FDA's guidelines for BIAS (<+/−5 mmHg) and STDEV(<8 mmHg), as outlined in the above-referenced AAMI SP-10:2002 referencestandard.

FIG. 14 shows a table describing drift calculated for the 4-hourmeasurement period for 22 of the above-mentioned subjects (one subjectwas excluded because their measurement period was less than 4 hours).Additionally, a follow-on study with 6 subjects investigated drift overan 8-hour measurement period. For this study, a single indexingmeasurement was performed at the beginning of the measurement, and allsubsequent measurements made over the 8-hour period were basedexclusively on PTT. In both cases, drift was calculated using a linearregression technique included in a SAS Statistical Analysis SoftwarePackage. More specifically, drift was estimated in a repeated-measuresmixed-effects general linear model using the ‘PROC MIXED’ model in SAS.This model includes a ‘fixed time effect’ model to estimate the drift.As shown in the table, drift over the 4-hour period is relatively small(−0.07 and −0.01 mmHg/hour for, respectively, SYS and DIA), essentiallywithin the error of the cNIBP measurement, and clinically insignificant.Drift for the 8-hour measurement period (−0.4 and −0.3 for,respectively, SYS and DIA) is slightly larger, although still within theerror of the cNIBP measurement and likely clinically insignificant.

Drift is an important parameter for characterizing the cNIBPmeasurement, as it essentially indicates how frequently the CompositeMethod must be indexed. Drift is generally attributed to a change(either gradual or rapid) in the subject's cardiovascular propertiesthat builds in after an indexing measurement. Such a change, forexample, may be attributed to a change in vascular compliance, tone,pre-injection period (PEP), left ventricular ejection time (LVET), orarterial dilation. Ideally, an indexing measurement would be performedat most once every 8-hours, as this time period corresponds with atypical nursing shift. In this case, the nurse would index a patient atthe beginning of the shift using the oscillometric approach describedherein. As shown in FIG. 24, the indexing measurement typically takesless than 1 minute, and is performed with a cuff-based system thatseamlessly integrates with the body-worn monitor used to make the cNIBPmeasurements. After the indexing measurement, the cuff-based system isremoved, and all follow-on cNIBP measurements are made cufflessly usingonly the ECG and PPG waveforms. At the 8-hour mark, the cuff-basedsystem is reapplied to the patient, and the process is repeated. At thispoint the subject's arterial properties and value for SYS, DIA, and MAPare recalculated and used for all subsequent measurements that takeplace over the next 8 hours.

Effect of Motion on PPG and ECG Waveforms

Motion is a parameter that confounds measurement of all vital signs, andis particularly detrimental to optical measurements, such as those usedin the Composite Method for cNIBP and pulse oximetry. For this reason itis important for the body-worn monitor to both recognize motion and,ideally, accurately determine the vital sign in its presence.

In a preferred embodiment, motion, posture, arm height, and activitylevel are determined from a patient by analyzing signals from threeseparate accelerometers integrated within the body-worn monitor. Asshown in detail in FIG. 24, the accelerometers are integrated into themonitor's cabling and wrist-worn transceiver. Each measures three uniquesignals, each corresponding to the x, y, and z-axes of the body portionto which the accelerometer attaches. These signals are then processedwith a series of algorithms, some of which are described in thefollowing patent application, the contents of which are incorporatedherein by reference: BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FORDETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20,2009). A software framework generates a series of alarms/alerts based onthreshold values that are either preset or determined in real time. Theframework additionally includes a series of ‘heuristic’ rules that takethe patient's activity state and motion into account, and process thevital signs accordingly. These rules, for example, indicate that awalking patient is likely breathing and has a regular heart rate, evenif their motion-corrupted vital signs suggest otherwise. They aredescribed in the following patent application, the contents of which arefully incorporated herein by reference: BODY-WORN MONITOR FEATURINGALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S.Ser. No. 12/469,182; filed May 20, 2009).

Measuring cNIBP During Motion

A variety of techniques can be used to remove motion artifacts fromsignals used to measure cNIBP, and particularly from the PPG waveformused in this measurement. For example, as described in detail below, asingle thumb-worn sensor measures PPG waveforms with both red (˜660 nm)and infrared (˜905 nm) wavelengths to determine an SpO2 measurement.Both PPGs waveforms are affected by motion, and can be collectivelyprocessed to remove motion artifacts to some degree. FIGS. 15-17 showresults from this analysis. FIG. 15, for example, shows both IR (toptrace) and RED (second trace) PPG waveforms measured as a function oftime over a 60-second period. Both waveforms were simultaneouslymeasured from the base of the patient's thumb using the sensor shown inFIG. 24. Each waveform features a series of pulses, similar to thoseshown in FIG. 7A, indicating a heartbeat-induced volumetric expansion invasculature lying beneath the optical sensor located at the base of thepatient's thumb. A period of motion beginning at about 40 seconds andlasting for about 10 seconds, and indicated with the box 220, corruptsboth PPG waveforms to the point that a clear, well-defined pulse cannotbe measured. In this case, motion consisted of rapid, large-scale motionof the patient's hand. The third trace in FIG. 15 shows the IR PPGwaveform divided by the RED PPG waveform. Division reduces the relativeamplitudes between motion artifacts shown in the box 220 and theheartbeat-induced pulses shown before and after this period, althoughnot to the point that individual pulses can be determined during theperiod of motion. However, processing the divided signal with a digitalbandpass filter yields a resultant signal shown in the bottom trace thathas clear, well-defined pulses. In this case, the bandpass filter wasimplemented with an IIR response function with a bandpass ranging from0.001→5 Hz. The resultant pulses, while still somewhat distorted bymotion, may be used to determine a PTT and consequently a cNIBPmeasurement.

FIG. 16 shows a similar result. In this case, however, the RED PPG issubtracted from the IR PPG to yield the third trace. Here, the relativeamplitude between heartbeat-induced pulses (shown before and after thebox 222 indicating the period of motion) and the motion-affected signalis slightly larger than that resulting from division, as shown in FIG.15, indicating that subtraction may be preferable to division for thisalgorithm. The resultant signal is processed with the same digitalbandpass filter described above to yield the bottom trace in the figure.Again, such processing yields pulses with reasonable signal-to-noiseratios, even in the presence of substantial motion. Following thisprocessing, pulses within the resultant PPG may be used to determinecNIBP, as described above.

Importantly, collective processing of both the RED and IR PPGs signals,combined with digital filtering, is significantly more effective atremoving motion artifacts than simply filtering the signals bythemselves. FIG. 17, for example, shows both the IR (top trace) and RED(second trace) PPG waveforms following processing with the same digitalbandpass filter used to generate the data shown in FIGS. 15 and 16. Inthis case the period of motion is indicated by the box 225. Even afterprocessing, these waveforms lack any clear, well-defined pulses duringthe period of motion. This indicates that filtering a single waveform islikely not adequate for removing motion-induced artifacts. Suchwaveforms, for example, would not be suitable for PTT-based cNIBPmeasurements. In contrast, the third trace in FIG. 17 shows the filteredPPG waveforms after the RED PPG is subtracted from the IR PPG. In thiscase no additional filtering is performed. The resultant waveform, likethose shown in the bottom traces of FIGS. 15 and 16, featureswell-defined pulses that can be subsequently processed to determinePTT-based cNIBP.

FIGS. 18 and 19 show an alternative method for collectively using bothECG 250 a,b and PPG 252 a,b waveforms to measure cNIBP in the presenceof motion. This method is based on the fundamental principal that ECGwaveforms 250 a,b, which rely on electrical signals measured from thepatient's chest, are relatively immune from motion artifacts, making itrelatively easy to measure HR values therefrom even when large-scalemotion is present. In contrast, PPG waveforms 252 a,b measured withoptical means are relatively susceptible to motion artifacts. Similar tothe method described above, it is the collective processing of thesesignals that yields accurate cNIBP measurement even when the patient ismoving.

Collective processing of both HR and PTT determined from ECG and PPGwaveforms yields a methodology for approximating PTT during periods ofmotion. This algorithm features analyzing the patient's current HR and apreceding array of paired values of HR/PTT using a continuous linearfitting approach, and then using these parameters resulting from the fitto estimate PTT. The theory behind the algorithm is as follows.Referring to FIG. 18, when no motion is present both the ECG 250 a andPPG 252 a waveforms are relatively noise-free. In this case cNIBP isdetermined from PTT (i.e. cNIBP˜F[PTT]) using the Composite Method,which processes the time separating the QRS complex within the ECG 250 aand the base of the PPG waveform 252 a. When motion is present, the ECGwaveform 250 b remains relatively noise-free, but the PPG waveform 252 bis corrupted by noise. In this case, cNIBP is calculated from HR (i.e.cNIBP˜F[HR]) using a real-time, evolving relationship between these twoparameters. As indicated by the graph in the lower portion of FIG. 18,HR and PTT have little correlation when evaluated over long periods oftime (e.g. 10 minutes or longer), but can have reasonable (or in factvery good) correlations when evaluated over very short periods of time(e.g. less than 2 minutes). Such correlation is indicated by the box253. This is because cardiac output, which relates HR and cNIBP, istypically constant for these short periods. FIG. 19 illustrates thisprincipal in more detail. As shown by the boxes 230, 232, a relationshipbetween HR and PTT can be determined by analyzing a preceding array ofpaired HR/PTT values, collected when the patient is not moving, with asimple linear regression model. These arrays, shown in the figure as[HR/PTT]_(i) and [HR/PTT]_(i+1), are collected over a period ΔT, whichis typically between 20 and 60 seconds. The linear model used to fit thearray returns a corresponding slope (M_(HR/PTT,i)), y-intercept(B_(HR/PTT,i)), and correlation value (r^2_(HR/PTT,i)). At a subsequenttime, indicated by the arrows 231, 233, the patient begins to move andparameters from the linear model can be used along with a current,motion-immune HR value to estimate a value of PTT called an ‘effective’PTT (PTT*). Specifically, PTT* is calculated using Equation (12) below,and then used in place of PTT to calculate cNIBP as described above.PTT*=HR×M _(HR/PTT,i) +B _(HR+PRR,i)  (12)

The relationship between HR and PTT determined with the linear model ismost accurate when the HR and PTT data points are collected immediatelyprior to the period of motion. If patient motion continues, then thismodel can be used along with HR for a period of time of up to 5 minutesto calculate cNIBP values. If patient motion persists past this period,then cNIBP cannot typically be accurately calculated, and thismethodology should not be used. Typically this approach works best ifcorrelation between HR and PTT, as indicated by r^2_(HR/PTT,i), isrelatively strong. In preferred embodiments, r^2_(HR/PTT,i) is greaterthan about 0.5 for the algorithm to be implemented. If r^2_(HR/PTT,i) isless than this value the algorithm is not implemented, and a bloodpressure value is assumed to be corrupted by motion, and thus notreported.

FIGS. 20A,B and 21A,B indicate the efficacy of this approach. FIGS. 20Aand 21A show time-dependent traces of SYS, measured with a femoralA-line, and a ‘reconstructed’ SYS determined using Equation (11). Thesegraphs were generated using data measured during the 23-subject ICUstudy, described above. FIGS. 20B and 21B plot the same data in FIGS.20A and 21A in a different way, showing a point-to-point correlationbetween SYS measured with the femoral arterial line and ‘reconstructed’SYS measured with the method according to the invention. As is clearfrom these graphs, reconstructing SYS values using this approach yieldsbetter agreement and higher correlation when the subject's bloodpressure undergoes only small amounts of volatility. For example, FIGS.20A,B show the ‘best’ results from the group of 23 subjects. Here, thesubject's blood pressure is relatively stable, and the reconstructed SYSagrees extremely well with SYS measured using the femoral arterial line(STDEV=2.0 mmHg; BIAS=0.2 mmHg). FIGS. 21A,B show data from a subjectwith highly volatile blood pressure. This case represents the ‘worst’agreement for the 23 subjects, likely because the volatility in cNIBPalso results in a corresponding volatility in cardiac output. This, inturn, makes it more difficult to accurately reconstruct the subject'scNIBP, and results in slightly worse correlation (STDEV=15.8 mmHg;BIAS=0.6 mmHg) between arterial line and reconstructed SYS.

Processing Accelerometer Waveforms to Determine Posture

In addition to motion, a patient's posture can influence how theabove-described system generates alarms/alerts from cNIBP and othervital signs. For example, the alarms/alerts related to cNIBP may varydepending on whether the patient is lying down or standing up. FIG. 22indicates how the body-worn monitor can determine motion-relatedparameters (e.g. degree of motion, posture, and activity level) from apatient 110 using time-dependent accelerometer waveforms continuouslygenerated from the three accelerometers 262, 263, 264 worn,respectively, on the patient's chest, bicep, and wrist. The height ofthe patient's arm can affect the cNIBP measurement, as blood pressurecan vary significantly due to hydrostatic forces induced by changes inarm height. Moreover, this phenomenon can be detected and exploited tocalibrate the cNIBP measurement, as described in detail in theabove-referenced patent application, the contents of which have beenpreviously incorporated by reference: BODY-WORN VITAL SIGN MONITOR WITHSYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094;filed May 20, 2009). As described in this document, arm height can bedetermined using DC signals from the accelerometers 263, 264 disposed,respectively, on the patient's bicep and wrist. Posture, in contrast,can be exclusively determined by the accelerometer 262 worn on thepatient's chest. An algorithm operating on the wrist-worn transceiverextracts DC values from waveforms measured from this accelerometer andprocesses them with an algorithm described below to determine posture.

Specifically, torso posture is determined for a patient 260 using anglesdetermined between the measured gravitational vector and the axes of atorso coordinate space 261. The axes of this space 261 are defined in athree-dimensional Euclidean space where {right arrow over (R)}_(CV) isthe vertical axis, {right arrow over (R)}_(CH) is the horizontal axis,and {right arrow over (R)}_(CN) is the normal axis. These axes must beidentified relative to a ‘chest accelerometer coordinate space’ beforethe patient's posture can be determined.

The first step in determining a patient's posture is to identifyalignment of {right arrow over (R)}_(CV) in the chest accelerometercoordinate space. This can be determined in either of two approaches. Inthe first approach, {right arrow over (R)}_(CV) is assumed based on atypical alignment of the body-worn monitor relative to the patient.During a manufacturing process, these parameters are then preprogrammedinto firmware operating on the wrist-worn transceiver. In this procedureit is assumed that accelerometers within the body-worn monitor areapplied to each patient with essentially the same configuration. In thesecond approach, {right arrow over (R)}_(CV) is identified on apatient-specific basis. Here, an algorithm operating on the wrist-worntransceiver prompts the patient (using, e.g., video instructionoperating on the wrist-worn transceiver, or audio instructionstransmitted through a speaker) to assume a known position with respectto gravity (e.g., standing upright with arms pointed straight down). Thealgorithm then calculates {right arrow over (R)}_(CV) from DC valuescorresponding to the x, y, and z axes of the chest accelerometer whilethe patient is in this position. This case, however, still requiresknowledge of which arm (left or right) the monitor is worn on, as thechest accelerometer coordinate space can be rotated by 180 degreesdepending on this orientation. A medical professional applying themonitor can enter this information using the GUI, described above. Thispotential for dual-arm attachment requires a set of two predeterminedvertical and normal vectors which are interchangeable depending on themonitor's location. Instead of manually entering this information, thearm on which the monitor is worn can be easily determined followingattachment using measured values from the chest accelerometer values,with the assumption that {right arrow over (R)}_(CV) is not orthogonalto the gravity vector.

The second step in the procedure is to identify the alignment of {rightarrow over (R)}_(CN) in the chest accelerometer coordinate space. Themonitor determines this vector in the same way it determines {rightarrow over (R)}_(CV) using one of two approaches. In the first approachthe monitor assumes a typical alignment of the chest-worn accelerometeron the patient. In the second approach, the alignment is identified byprompting the patient to assume a known position with respect togravity. The monitor then calculates {right arrow over (R)}_(CN) fromthe DC values of the time-dependent accelerometer waveform.

The third step in the procedure is to identify the alignment of {rightarrow over (R)}_(CH) in the chest accelerometer coordinate space. Thisvector is typically determined from the vector cross product of {rightarrow over (R)}_(CV) and {right arrow over (R)}_(CN), or it can beassumed based on the typical alignment of the accelerometer on thepatient, as described above.

A patient's posture is determined using the coordinate system describedabove and in FIG. 22, along with a gravitational vector {right arrowover (R)}_(G) that extends normal from the patient's chest. The anglebetween {right arrow over (R)}_(CVv) and {right arrow over (R)}_(G) isgiven by equation (13):

$\begin{matrix}{{\theta_{VG}\lbrack n\rbrack} = {\arccos( \frac{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{CV}}{{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{CV}}} )}} & (13)\end{matrix}$where the dot product of the two vectors is defined as:{right arrow over (R)} _(G) [n]·{right arrow over (R)} _(CV)=(y _(Cx)[n]×r _(CVx))+(y _(CVy) [n]×r _(CVy))+(y _(CZ) [n]×r _(CVz))  (14)

The definition of the norms of {right arrow over (R)}_(G) and {rightarrow over (R)}_(CV) are given by equations (15) and (16):∥{right arrow over (R)} _(G) [n]∥=√{square root over ((y _(Cx) [n])²+(y_(Cy) [n])²+(y _(Cz) [n])²)}{square root over ((y _(Cx) [n])²+(y _(Cy)[n])²+(y _(Cz) [n])²)}{square root over ((y _(Cx) [n])²+(y _(Cy)[n])²+(y _(Cz) [n])²)}  (15)∥{right arrow over (R)} _(CV)∥=√{square root over ((r _(CVx))²+(r_(CVy))²+(r _(CVz))²)}{square root over ((r _(CVx))²+(r _(CVy))²+(r_(CVz))²)}{square root over ((r _(CVx))²+(r _(CVy))²+(r _(CVz))²)}  (16)

As indicated in equation (5), the monitor compares the vertical angleθ_(VG) to a threshold angle to determine whether the patient is vertical(i.e. standing upright) or lying down:if θ_(VG)≦45° then Torso State=0, the patient is upright  (17)

If the condition in equation (17) is met the patient is assumed to beupright, and their torso state, which is a numerical value equated tothe patient's posture, is equal to 0. The patient is assumed to be lyingdown if the condition in equation (17) is not met, i.e. θ_(VG)>45degrees. Their lying position is then determined from angles separatingthe two remaining vectors, as defined below.

The angle θ_(NG) between {right arrow over (R)}_(CN) and {right arrowover (R)}_(G) determines if the patient is lying in the supine position(chest up), prone position (chest down), or on their side. Based oneither an assumed orientation or a patient-specific calibrationprocedure, as described above, the alignment of {right arrow over(R)}_(CN) is given by equation (18), where i, j, k represent the unitvectors of the x, y, and z axes of the chest accelerometer coordinatespace respectively:{right arrow over (R)} _(CN) =r _(CNx) î+r _(CNy) ĵ+r _(CNz) {circumflexover (k)}  (18)

The angle between {right arrow over (R)}_(CN) and {right arrow over(R)}_(G) determined from DC values extracted from the chestaccelerometer waveform is given by equation (19):

$\begin{matrix}{{\theta_{NG}\lbrack n\rbrack} = {\arccos( \frac{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{CN}}{{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{CN}}} )}} & (19)\end{matrix}$

The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position the patient is lying in, as shown in equation (20):if θ_(NG)≦35° then Torso State=1, the patient is supineif θ_(NG)≧135° then Torso State=2, the patient is prone  (20)

If the conditions in equation (20) are not met then the patient isassumed to be lying on their side. Whether they are lying on their rightor left side is determined from the angle calculated between thehorizontal torso vector and measured gravitational vectors, as describedabove.

The alignment of {right arrow over (R)}_(CH) is determined using eitheran assumed orientation, or from the vector cross-product of {right arrowover (R)}_(CV) and {right arrow over (R)}_(CN) as given by equation(21), where i, j, k represent the unit vectors of the x, y, and z axesof the accelerometer coordinate space respectively. Note that theorientation of the calculated vector is dependent on the order of thevectors in the operation. The order below defines the horizontal axis aspositive towards the right side of the patient's body.{right arrow over (R)} _(CH) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflexover (k)}={right arrow over (R)} _(CV) ×{right arrow over (R)}_(CN)  (21)The angle θ_(HG) between {right arrow over (R)}_(CH) and {right arrowover (R)}_(G) is determined using equation (22):

$\begin{matrix}{{\theta_{HG}\lbrack n\rbrack} = {\arccos( \frac{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{CH}}{{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{CH}}} )}} & (22)\end{matrix}$

The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by equation (23):if θ_(HG)≧90° then Torso State=3, the patient is on their right sideif θ_(NG)<90° then Torso State=4, the patient is on their leftside  (23)

Table 1 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon on a remote computer:

TABLE 1 postures and their corresponding torso states Posture TorsoState standing upright 0 supine: lying on back 1 prone: lying on chest 2lying on right side 3 lying on left side 4 undetermined posture 5

FIGS. 23A and 23B show, respectively, graphs of time-dependentaccelerometer waveforms measured along the x, y, and z-axes (FIG. 23A),and the torso states (i.e. postures; FIG. 23B) determined from thesewaveforms for a moving patient, as described above. As the patientmoves, the DC values of the accelerometer waveforms measured by thechest accelerometer vary accordingly, as shown in FIG. 23A. Thebody-worn monitor processes these values as described above tocontinually determine {right arrow over (R)}_(G) and the variousquantized torso states for the patient, as shown in FIG. 23B. The torsostates yield the patient's posture as defined in Table 1. For this studythe patient rapidly alternated between standing, lying on their back,chest, right side, and left side within a time period of about 160seconds. As described above, different alarm/alert conditions (e.g.threshold values) for vital signs can be assigned to each of thesepostures, or the specific posture itself may result in an alarm/alert.Additionally, the time-dependent properties of the graph can be analyzed(e.g. changes in the torso states can be counted) to determine, forexample, how often the patient moves in their hospital bed. This numbercan then be equated to various metrics, such as a ‘bed sore index’indicating a patient that is so stationary in their bed that lesions mayresult. Such a state could then be used to trigger an alarm/alert to thesupervising medical professional.

Body-Worn Monitor for Measuring cNIBP

FIGS. 24A and 24B show how the body-worn monitor 300 described aboveattaches to a patient 270 to measure cNIBP and other vital signs. Adetailed description of this monitor is provided by the followingco-pending patent application, the contents of which are incorporatedherein by reference: BODY-WORN VITAL SIGN MONITOR (U.S. Ser. No.12/560,087; filed Sep. 15, 2009). These figures show two configurationsof the system: FIG. 24A shows the system used during the indexingportion of the Composite Method, and includes a pneumatic, cuff-basedsystem 285, while FIG. 24B shows the system used for subsequent cNIBPmeasurements. The indexing measurement typically takes about 60 seconds,and is typically performed once every 4 hours. Once the indexingmeasurement is complete the cuff-based system 285 is typically removedfrom the patient. The remainder of the time the monitor 300 performs thecNIBP measurements.

The body-worn monitor 300 features a wrist-worn transceiver 272,described in more detail in FIG. 25, featuring a touch panel interface273 that displays cNIBP values and other vital signs. A wrist strap 290affixes the transceiver 272 to the patient's wrist like a conventionalwristwatch. A flexible cable 292 connects the transceiver 272 to a pulseoximeter probe 294 that wraps around the base of the patient's thumb.During a measurement, the probe 294 generates a time-dependent PPGwaveform which is processed along with an ECG to measure cNIBP and SpO2.This provides an accurate representation of blood pressure in thecentral regions of the patient's body, as described above.

To determine accelerometer waveforms the body-worn monitor 300 featuresthree separate accelerometers located at different portions on thepatient's arm and chest. The first accelerometer is surface-mounted on acircuit board in the wrist-worn transceiver 272 and measures signalsassociated with movement of the patient's wrist. As described above,this motion can also be indicative of that originating from thepatient's fingers, which will affect the SpO2 measurement. The secondaccelerometer is included in a small bulkhead portion 296 included alongthe span of the cable 282. During a measurement, a small piece ofdisposable tape, similar in size to a conventional bandaid, affixes thebulkhead portion 296 to the patient's arm. In this way the bulkheadportion 296 serves two purposes: 1) it measures a time-dependentaccelerometer waveform from the mid-portion of the patient's arm,thereby allowing their posture and arm height to be determined asdescribed in detail above; and 2) it secures the cable 282 to thepatient's arm to increase comfort and performance of the body-wornmonitor 300, particularly when the patient is ambulatory. The thirdaccelerometer is mounted in a bulkhead component 274 that connectsthrough cables 280 a-c to ECG electrodes 278 a-c. These signals are thendigitized, transmitted through the cable 282 to the wrist-worntransceiver 272, where they are processed with an algorithm as describedabove to determine respiration rate, as described in the followingco-pending patent applications, the contents of which are incorporatedherein by reference: BODY-WORN MONITOR FOR MEASURING RESPIRATION RATE(U.S. Ser. No. 12/559,442; filed Sep. 14, 2009).

The cuff-based module 285 features a pneumatic system 276 that includesa pump, valve, pressure fittings, pressure sensor, analog-to-digitalconverter, microcontroller, and rechargeable Li:ion battery. During anindexing measurement, the pneumatic system 276 inflates a disposablecuff 284 and performs two measurements according to the CompositeMethod: 1) it performs an inflation-based measurement of oscillometry todetermine values for SYS_(INDEX), DIA_(INDEX), and MAP_(INDEX); and 2)it determines a patient-specific slope describing the relationshipbetween PTT and MAP. These measurements are described in detail in theabove-referenced patent application entitled: ‘VITAL SIGN MONITOR FORMEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSUREWAVEFORMS’ (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contentsof which have been previously incorporated herein by reference.

The cuff 284 within the cuff-based pneumatic system 285 is typicallydisposable and features an internal, airtight bladder that wraps aroundthe patient's bicep to deliver a uniform pressure field. During theindexing measurement, pressure values are digitized by the internalanalog-to-digital converter, and sent through a cable 286 according to aCAN protocol, along with SYS_(INDEX), DIA_(INDEX), and MAP_(INDEX), tothe wrist-worn transceiver 272 for processing as described above. Oncethe cuff-based measurement is complete, the cuff-based module 285 isremoved from the patient's arm and the cable 286 is disconnected fromthe wrist-worn transceiver 272. cNIBP is then determined using PTT, asdescribed in detail above.

To determine an ECG, the body-worn monitor 300 features a small-scale,three-lead ECG circuit integrated directly into the bulkhead 274 thatterminates an ECG cable 282. The ECG circuit features an integratedcircuit that collects electrical signals from three chest-worn ECGelectrodes 278 a-c connected through cables 280 a-c. As described above,the ECG electrodes 278 a-c are typically disposed in a conventionalEinthoven's Triangle configuration which is a triangle-like orientationof the electrodes 278 a-c on the patient's chest that features threeunique ECG vectors. From these electrical signals the ECG circuitdetermines up to three ECG waveforms, which are digitized using ananalog-to-digital converter mounted proximal to the ECG circuit, andsent through the cable 282 to the wrist-worn transceiver 272 accordingto the CAN protocol. There, the ECG and PPG waveforms are processed todetermine the patient's blood pressure. Heart rate and respiration aredetermined directly from the ECG waveform using known algorithms, suchimpedance pneumography, as well as those described above. The cablebulkhead 274 also includes an accelerometer that measures motionassociated with the patient's chest as described above.

As described above, there are several advantages of digitizing ECG andaccelerometer waveforms prior to transmitting them through the cable282. First, a single transmission line in the cable 282 can transmitmultiple digital waveforms, each generated by different sensors. Thisincludes multiple ECG waveforms (corresponding, e.g., to vectorsassociated with three, five, and twelve-lead ECG systems) from the ECGcircuit mounted in the bulkhead 274, along with waveforms associatedwith the x, y, and z-axes of accelerometers mounted in the bulkheads274, 296. More sophisticated ECG circuits (e.g. five and twelve-leadsystems) can plug into the wrist-worn transceiver to replace thethree-lead system shown in FIGS. 24A and 24B.

FIG. 25 shows a close-up view of the wrist-worn transceiver 272. Asdescribed above, it attaches to the patient's wrist using a flexiblestrap 290 which threads through two D-ring openings in a plastic housing306. The transceiver 272 features a touch panel display 320 that rendersa GUI 273 which is altered depending on the viewer (typically thepatient or a medical professional). Specifically, the transceiver 272includes a small-scale infrared barcode scanner 302 that, during use,can scan a barcode worn on a badge of a medical professional. Thebarcode indicates to the transceiver's software that, for example, anurse or doctor is viewing the user interface. In response, the GUI 273displays vital sign data and other medical diagnostic informationappropriate for medical professionals. Using this GUI 273, the nurse ordoctor, for example, can view the vital sign information, set alarmparameters, and enter information about the patient (e.g. theirdemographic information, medication, or medical condition). The nursecan press a button on the GUI 273 indicating that these operations arecomplete. At this point, the display 320 renders an interface that ismore appropriate to the patient, such as time of day and battery power.

The transceiver 272 features three CAN connectors 304 a-c on the side ofits upper portion, each which supports the CAN protocol and wiringschematics, and relays digitized data to the internal CPU. Digitalsignals that pass through the CAN connectors include a header thatindicates the specific signal (e.g. ECG, ACC, or pressure waveform fromthe cuff-based module) and the sensor from which the signal originated.This allows the CPU to easily interpret signals that arrive through theCAN connectors 304 a-c, such as those described above corresponding toECG waveforms, and means that these connectors are not associated with aspecific cable. Any cable connecting to the transceiver can be pluggedinto any connector 304 a-c. As shown in FIG. 24A, the first connector304 a receives the cable 282 that transports a digitized ECG waveformdetermined from the ECG circuit and electrodes, and digitizedaccelerometer waveforms measured by accelerometers in the cable bulkhead274 and the bulkhead portion 296 associated with the ECG cable 282.

The second CAN connector 304 b shown in FIG. 25 receives the cable 286that connects to the pneumatic cuff-based system 285 used for thepressure-dependent indexing measurement (shown in FIG. 24A). Thisconnector 304 b receives a time-dependent pressure waveform delivered bythe pneumatic system 285 to the patient's arm, along with values forSYS_(INDEX), DIA_(INDEX), and MAP_(INDEX) values determined during theindexing measurement. The cable 286 unplugs from the connector 304 bonce the indexing measurement is complete, and is plugged back in afterapproximately four hours for another indexing measurement.

The final CAN connector 304 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, ventilator, oret-CO2 measurement system. As described above, digital informationgenerated by these systems will include a header that indicates theirorigin so that the CPU can process them accordingly.

The transceiver includes a speaker 301 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VOIP). For example, using the speaker 301 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in FIG. 25, and use this as a communication device.In this application, the transceiver 272 worn by the patient functionsmuch like a conventional cellular telephone or ‘walkie talkie’: it canbe used for voice communications with the medical professional and canadditionally relay information describing the patient's vital signs andmotion. The speaker can also enunciate pre-programmed messages to thepatient, such as those used to calibrate the chest-worn accelerometersfor a posture calculation, as described above.

Multi-Pixel Sensors for Measuring PPG Waveforms in the Presence ofMotion

As described above and shown in FIG. 24A, the thumb-worn sensortypically used in the body-worn monitor features a photodetector with asingle-pixel light-detecting region. Typically this pixel has an area of2-4 mm^2. During the Composite Method, the photodetector measures a PPGwaveform, which is collectively processed with the ECG waveform todetermine cNIBP. Alternatively, as shown in FIGS. 26-29, an opticalsensor 351 featuring a multi-pixel detector 354 can be used in place ofa single-pixel detector to measure a PPG waveform. Such a detector 354may be particularly effective at detecting signals when a patient is inmotion.

The multi-pixel sensor 351 features a soft, flexible substrate 352coated on its perimeter with an adhesive 356 designed to adhere to apatient's skin. As shown in FIG. 26, for optimal results the sensor 351is adhered to the forehead of a patient 350, and connects through acable 352 to a controller 353. In this embodiment, the controller 353and cable 352 are similar, respectively, to the wrist-worn transceiver273 and cable 292 shown in FIG. 24A. Experiments indicate that theforehead is the ideal sensor location for this alternative embodiment,presumably because tissue supporting underlying vasculature isrelatively thin and buttressed on its inner side with bone from thepatient's skull. This physiology minimizes motion between the sensor 351and arteries in the forehead that can cause artifacts in the PPG.Additionally, presence of the skull limits the compressibility of thethin, underlying tissue, which in turn minimizes motion-induced flow ofboth blood and interstitial fluids within the tissue. These factors,particularly when coupled with the forehead's large availablemeasurement area relatively small degree of motion, make this locationideal for the sensor 351.

The sensor 351 typically features a square footprint and includes fourdual-wavelength LEDs 353 a-d positioned in each of its corners. Each LED353 a-d emits both red and infrared optical wavelengths, as describedabove. An adjustable voltage bias supplied to each LED determines itsemitted wavelength. During a measurement, the substrate 352 attaches tothe patient's forehead with the adhesive 356, allowing the LEDs 353 a-dto deliver a relatively uniform optical field to the tissue underneath.The optical field is partially absorbed by pulsating blood in theunderlying vasculature according to the Beer-Lambert law, as describedabove. This modulates the optical field, which is then detected in areflection-mode geometry by the multi-pixel detector 354. Each pixelelement in the detector 354 typically has an area of 1-2 mm^2, andgenerates a unique, analog electrical field which propagates through aseries of electrical interconnects 355 to an electrical system 357featuring a multichannel analog-to-digital converter (A/D) coupled to acircuit for multiplexing and demultiplexing (MUX) the resulting digitalsignals. These components digitize the analog signals from each pixelelement and process them to form a single PPG waveform, similar to thatshown in FIG. 7A.

FIGS. 28A,B and 29A,B indicate how the multi-pixel sensor 351 a,b (FIGS.29A,B) may be superior at detecting PPG waveforms during motion whencompared to a conventional single-pixel detector 385 a,b (FIGS. 28A,B).Blood is a viscoelastic fluid, and during motion will ebb and flow in apatient's arterial system according to Newtonian physics. This affect,informally referred to herein as ‘blood sloshing’, can be characterizedby time-dependent properties (e.g. rise and fall times) that are similarin their frequency makeup to an actual pulse in a PPG waveform. This iswhy, for example, motion-induced artifacts shown in the waveforms inFIGS. 15 and 16 look similar to the actual pulses in the PPG. Bloodsloshing causes a bolus of blood 390 a,b to move in and out of theregion measured with a single-pixel detector 387 a,b shown in FIGS.28A,B. The single detector 387 a,b has no ability to track thetime-dependent dynamics of the bolus of blood 390 a,b as it moves acrossthe detector field. The bolus 390 a moves into the detector field attime t₀, propagates across the field, and finally moves out at timet₀+Δt. Because it cannot be isolated, the bolus 390 a,b results in anartifact in the PPG waveform that is difficult, if not impossible, toremove with conventional means, such as a digital filter.

In contrast, FIGS. 29A,B show how a multi-pixel detector 351 a,b cantrack the bolus of blood 380 a,b as it moves across the detector area.This allows it to be isolated and removed from the PPG waveform usingthe multiplexing/demultiplexing circuitry 357 described with referenceto FIG. 27. In this case, for example, the bolus 380 a,b moves across adiagonal line in the detector field; only pixels lying along this linewill yields signals affected by the motion-related artifact. Thesesignals will show different behavior than conventional PPG waveforms,which will be detected by pixels in the upper left-hand and lowerright-hand portions of the multi-array detector 354 a,b. Once detected,signals from each pixel can be processed with a variety ofsignal-processing techniques, such as those used to process videoimages, to deconvolute artifacts arising from the bolus. Ultimately thiscan yields a relatively noise-free PPG waveform, which can then beprocessed with the Composite Method to determine cNIBP.

High-Level Algorithm for Measuring All Vital Signs

FIG. 30 provides a flow chart that shows a high-level algorithm 399,including the Composite Method, used to monitor vital signs from ahospitalized patient. The initiation phase (step 400) of the algorithm399 begins with collection of time-dependent PPG, ECG, and accelerometerwaveforms using analog and digital circuitry within the body-wornmonitor. PPG waveforms are measured using an optical sensor attached tothe patient's thumb, while ECG waveforms are measured with a series ofelectrodes (typically three or five) attached to the patient's chest.Three accelerometers, integrated within the body-worn monitor's cablingand wrist-worn transceiver, each measure three digital accelerometerwaveforms corresponding to an x, y, or z-axis. Once collected, the PPGand accelerometer waveforms are digitally filtered (step 401) so thattime-dependent properties can be extracted and processed as described indetail above. The pressure waveform, which is generated during anindexing measurement using a pneumatic system and cuff wrapped aroundthe patient's bicep, is measured during inflation and processed usingoscillometry to determine SYS_(INDEX), DIA_(INDEX), and MAP_(INDEX)values (step 402). Alternatively, SYS_(INDEX) can be determined directlyby processing the PPG in the presence of applied pressure during theindexing measurement, as described above with reference to FIG. 8 (step403). PTT is measured as a function of applied pressure during theindexing measurement, and is processed to determine a personal,patient-specific slope (step 404). Motion can complicate measurement ofthe above-described parameters, and is determined by processingtime-dependent signals from the three accelerometers attached to thepatient and connected to the body-worn monitor. These signals arecollected and processed to determine the degree of motion-based signalcorruption (step 405), and to additionally determine the patient'sposture and activity level (step 407). If motion is determined to bepresent, cNIBP can be estimated using the read-through motion algorithmdescribed above with reference to FIGS. 18 and 19 (step 408).

When minimal or no motion is present, the patient-specific slope, alongwith blood pressure values determined with oscillometry during theindexing measurements, are used with PTT values measured from the ECGand PPG waveforms to determine cNIBP (step 410). PPG waveforms measuredwith both red and infrared waveforms are additionally processed todetermine SpO2, as described above, using modified calculationparameters tailored for the base of the thumb (step 411).

The body-worn monitor makes the above-described measurements forPTT-based cNIBP by collecting data for 20-second periods, and thenprocessing these data with a variety of statistical averaging techniquesas described above. Additionally, algorithms that process ECG andaccelerometer waveforms using adaptive filtering can determinerespiration rate, as described in the following patent application, thecontents of which have been previously incorporated herein by reference:BODY-WORN MONITOR FOR MEASURING RESPIRATION RATE (U.S. Ser. No.12/559,442; filed Sep. 14, 2009) (step 412). Heart rate and temperatureare then determined as described in the following patent application,the contents of which have been already incorporated herein byreference: BODY-WORN VITAL SIGN MONITOR (U.S. Ser. No. 12/560,087; filedSep. 15, 2009) (step 413).

All the vital signs described above are typically calculated with atechnique for rolling averages that updates them every second. Every 4-8hours the indexing measurement is repeated, either with a completeinflation-based measurement (step 402), or one based on partialinflation (step 403) as described above.

Other Embodiments

In addition to those methods described above, a number of additionalmethods can be used to calculate blood pressure from the PPG and ECGwaveforms. These are described in the following co-pending patentapplications, the contents of which are incorporated herein byreference: 1) CUFFLESS BLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS,INTERNET-BASED SYSTEM (U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2)CUFFLESS SYSTEM FOR MEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014;filed Apr. 7, 2004); 3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYINGWEB SERVICES INTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004);4) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILEDEVICE (U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 5) BLOODPRESSURE MONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S.Ser. No. 10/967,610; filed Oct. 18, 2004); 6) PERSONAL COMPUTER-BASEDVITAL SIGN MONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 7)PATCH SENSOR FOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No.10/906,315; filed Feb. 14, 2005); 8) PATCH SENSOR FOR MEASURING VITALSIGNS (U.S. Ser. No. 11/160,957; filed Jul. 18, 2005); 9) WIRELESS,INTERNET-BASED SYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OFPATIENTS IN A HOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719;filed Sep. 9, 2005); 10) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS(U.S. Ser. No. 11/162,742; filed Sep. 21, 2005); 11) CHEST STRAP FORMEASURING VITAL SIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005);12) SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING AGREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 13)BILATERAL DEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S.Ser. No. 11/420,281; filed May 25, 2006); 14) SYSTEM FOR MEASURING VITALSIGNS USING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652;filed May 26, 2006); 15) BLOOD PRESSURE MONITOR (U.S. Ser. No.11/530,076; filed Sep. 8, 2006); 16) TWO-PART PATCH SENSOR FORMONITORING VITAL SIGNS (U.S. Ser. No. 11/558,538; filed Nov. 10, 2006);and, 17) MONITOR FOR MEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES(U.S. Ser. No. 11/682,177; filed Mar. 5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other measurement techniques, such as conventional oscillometrymeasured during deflation, can be used to determine SYS for theabove-described algorithms. Additionally, processing components andsensors for measuring SpO2 similar to those described above can bemodified and worn on other portions of the patient's body. For example,sensors with finger-ring configurations can be worn on fingers otherthan the thumb. Or they can be modified to attach to other conventionalsites for measuring SpO2, such as the ear, forehead, and bridge of thenose. In these embodiments the processing component can be worn inplaces other than the wrist, such as around the neck (and supported,e.g., by a lanyard) or on the patient's waist (supported, e.g., by aclip that attaches to the patient's belt). In still other embodimentsthe probe and processing component are integrated into a single unit.

In other embodiments, a set of body-worn monitors can continuouslymonitor a group of patients, wherein each patient in the group wears abody-worn monitor similar to those described herein. Additionally, eachbody-worn monitor can be augmented with a location sensor. The locationsensor includes a wireless component and a location-processing componentthat receives a signal from the wireless component and processes it todetermine a physical location of the patient. A processing component(similar to that described above) determines from the time-dependentwaveforms at least one vital sign, one motion parameter, and an alarmparameter calculated from the combination of this information. Awireless transceiver transmits the vital sign, motion parameter,location of the patient, and alarm parameter through a wireless system.A remote computer system featuring a display and an interface to thewireless system receives the information and displays it on a userinterface for each patient in the group.

In embodiments, the interface rendered on the display at the centralnursing station features a field that displays a map corresponding to anarea with multiple sections. Each section corresponds to the location ofthe patient and includes, e.g., the patient's vital signs, motionparameter, and alarm parameter. For example, the field can display a mapcorresponding to an area of a hospital (e.g. a hospital bay or emergencyroom), with each section corresponding to a specific bed, chair, orgeneral location in the area. Typically the display renders graphicalicons corresponding to the motion and alarm parameters for each patientin the group. In other embodiments, the body-worn monitor includes agraphical display that renders these parameters directly on the patient.

Typically the location sensor and the wireless transceiver operate on acommon wireless system, e.g. a wireless system based on 802.11,802.15.4, or cellular protocols. In this case a location is determinedby processing the wireless signal with one or more algorithms known inthe art. These include, for example, triangulating signals received fromat least three different base stations, or simply estimating a locationbased on signal strength and proximity to a particular base station. Instill other embodiments the location sensor includes a conventionalglobal positioning system (GPS).

The body-worn monitor can include a first voice interface, and theremote computer can include a second voice interface that integrateswith the first voice interface. The location sensor, wirelesstransceiver, and first and second voice interfaces can all operate on acommon wireless system, such as one of the above-described systems basedon 802.11 or cellular protocols. The remote computer, for example, canbe a monitor that is essentially identical to the monitor worn by thepatient, and can be carried or worn by a medical professional. In thiscase the monitor associated with the medical professional features a GUIwherein the user can select to display information (e.g. vital signs,location, and alarms) corresponding to a particular patient. Thismonitor can also include a voice interface so the medical professionalcan communicate directly with the patient.

In other embodiments, a variety of software configurations can be run onthe body-worn monitor to give it a PDA-like functionality. Theseinclude, for example, Micro C OS®, Linux®, Microsoft Windows®, embOS,VxWorks, SymbianOS, QNX, OSE, BSD and its variants, FreeDOS, FreeRTOX,LynxOS, or eCOS and other embedded operating systems. The monitor canalso run a software configuration that allows it to receive and sendvoice calls, text messages, or video streams received through theInternet or from the nation-wide wireless network it connects to. Thebarcode scanner described with reference to FIG. 25 can also be used tocapture patient or medical professional identification information, orother such labeling. This information, for example, can be used tocommunicate with a patient in a hospital or at home. In otherembodiments, the device can connect to an Internet-accessible website todownload content, e.g., calibrations, software updates, text messages,and information describing medications, from an associated website. Asdescribed above, the device can connect to the website using both wired(e.g., CAN) or wireless (e.g., short or long-range wirelesstransceivers) means. In still other embodiments, ‘alert’ valuescorresponding to vital signs and the pager or cell phone number of acaregiver can be programmed into the device using its graphical userinterface. If a patient's vital signs meet an alert criteria, softwareon the device can send a wireless ‘page’ to the caregiver, therebyalerting them to the patient's condition. For additional patient safety,a confirmation scheme can be implemented that alerts other individualsor systems until acknowledgment of the alert is received.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A system for monitoring a blood pressure valuefrom a patient, comprising: (a) a first sensor comprising: a firstradiation source configured to emit optical radiation at a firstwavelength; a second radiation source configured to emit opticalradiation at a second wavelength; and a photodetector configured todetect radiation emitted by the first radiation source after it passesthrough a portion of the patient to generate a first photoplethysmogramwaveform, and radiation emitted by the second radiation source after itpasses through a portion of the patient to generate a secondphotoplethysmogram waveform; (b) a second sensor comprising: an ECGcircuit and at least two electrodes, the second sensor configured todetect electrical signals from the patient and process them to generatea third signal; and, (c) a processing component configured to be worn onthe patient's body and connected to the first sensor to receive thefirst and second photoplethysmogram waveforms, and to the second sensorto receive the third signal, the processing component programmed to: i)collectively process the first and second photoplethysmogram waveformsby dividing one photoplethysmogram waveform into the otherphotoplethysmogram waveform to generate a processed signal; ii) processthe processed signal with a digital filter to generate a filteredsignal; iii) analyze the filtered signal to determine a firsttime-dependent feature; iv) analyze the third signal or a processedversion thereof to determine a second time-dependent feature; v) analyzethe first and second time-dependent features to determine a timedifference; and vi) analyze the time difference to determine the bloodpressure value.
 2. The system of claim 1, wherein the first radiationsource emits an optical wavelength between 800-1000 nm.
 3. The system ofclaim 2, wherein the second radiation source emits an optical wavelengthbetween 590-700 nm.
 4. The system of claim 1, wherein the processingcomponent is programmed to process the processed signal with a digitalbandpass filter characterized by a passband between 0.01-5.0 Hz togenerate the filtered signal.
 5. The system of claim 1, wherein theprocessing component is programmed to analyze the filtered signal todetermine an onset point representing the feature related to bloodpressure.
 6. The system of claim 5, wherein the processing component isfurther configured to receive the third signal, and is programmed todetermine a QRS component from the third signal.
 7. The system of claim6, wherein the processing component is programmed to determine a timedifference between the onset point and a feature of the QRS component,and then process the time difference to determine the blood pressurevalue.
 8. The system of claim 1, further comprising a motion sensorconfigured to attach to a portion of patient, the motion sensorconfigured to generate a motion signal indicative of motion of theportion to which the motion sensor is attached.
 9. The system of claim8, wherein the motion sensor is an accelerometer.